• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.33-49
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• 2016

Structural Health Monitoring (SHM) has been attracting numerous research efforts around the world because it targets at monitoring structural conditions and performance to prevent catastrophic failure, and to provide quantitative data for engineers and infrastructure owners to design a reliable and economical asset management strategy. In the past decade, with supports from Australian Research Council (ARC), Cooperative Research Center for Infrastructure and Engineering Asset Management (CIEAM), CSIRO and industry partners, intensive research works have been conducted in the School of Civil, Environmental and Mining Engineering, University of Western Australia and Centre for Infrastructural Monitoring and Protection, Curtin University on various techniques of SHM. The researches include the development of hardware, software and various algorithms, such as various signal processing techniques for operational modal analysis, modal analysis toolbox, non-model based methods for assessing the shear connection in composite bridges and identifying the free spanning and supports conditions of pipelines, vibration based structural damage identification and model updating approaches considering uncertainty and noise effects, structural identification under moving loads, guided wave propagation technique for detecting debonding damage, and relative displacement sensors for SHM in composite and steel truss bridges. This paper aims at summarizing and reviewing the recent research advances on SHM of civil infrastructure in Western Australia.

• Smart Structures and Systems
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• v.20 no.2
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1400-1420
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• 2012

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• Computers and Concrete
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• v.4 no.5
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• pp.347-362
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• 2007

Due to heterogeneities at all scales, concrete exhibits significant variability in mechanical behavior from sample to sample. An understanding of the fundamental mechanical performance of concrete must therefore be embedded in a stochastic framework. The current work attempts to address the connection between a two-dimensional concrete mesostructure and the random local material properties associated within that mesostructure. This work builds on previous work that has focused on the random configuration of concrete mesostructures. This was accomplished by developing an understanding of the effects of variations in the mortar strength and the mortar-aggregate interfacial strength in given deterministic mesostructural configurations. The results are assessed through direct tension tests that are validated by comparing experimental results of two different, pre-arranged mesostructures, with the intent of isolating the effect of local variations in strength. Agreement is shown both in mechanical property values as well as the qualitative nature of crack initiation and propagation.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.329-339
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• 2019

The strategy for the management of earthquakes is shifting from post recovery to prevention; therefore, seismic performance management requires quantitative predictions of damage and the establishment of strategies for initial responses to earthquakes. Currently, seismic performance evaluation for seismic management in Korea consists of two stages: preliminary evaluation and detailed evaluation. Also, the priority of seismic performance management is determined in accordance with the preliminary evaluation. As a deterministic method, preliminary evaluation quantifies the physical condition and socio-economic importance of a facility by various predetermined indices, and the priority is decided by the relative value of the indices; however, with the deterministic method it is difficult to consider any uncertainty related to the return-year, epicenter, and propagation of seismic energy. Also this method cannot support tasks such as quantitative socio-economic damage and the provision of data for initial responses to earthquakes. Moreover, indirect damage is often greater than direct damage; therefore, a method to quantify damage is needed to enhance accuracy. In this paper, a Seismic Risk Assessment is used to quantify the cost of damage of road facilities in Pohang city and to support decision making.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Cross-Cultural Studies
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• v.25
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• pp.159-183
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• 2011

This study is for the understanding of the role of fortunetelling and the "Scripture of Fortunetelling(占察經)" through the relationship between the manners and customs of Koreans and the Buddhist Scripture dealing with the fortunetelling. It is meaningful to understand how it is related with Buddhism. As a result, I perceived that Koreans have lived with fortunetelling and that the role of fortunetelling in from private person to the country. I saw there were four big roles; to clean the uncertainty of the future to fulfill the expectations, to confirm the justice of what he or she did, to cure, and to choice. Looking into "Memorabilia of the Three Kingdoms(三國遺事)", I see that the Scripture of Fortunetelling and the ceremony of fortunetelling was popular and those great monks such as Wongwang, Wonhyo, Jinpyo and Jihye presided these ceremonies. The structure of the "Scripture of Fortunetelling(占察經)" has the hierarchy of Fortunetelling, Penance, Practice, Awakening, and Salvation of the world. So fortunetelling motivated every stage of practice. The role of fortunetelling in "Scripture of Fortunetelling(占察經)" is to intermediate. For this purpose it divided the attitude for the fortunetelling into two categories; for the urgent real life and just for enjoying. But they seriously dealt with both results. They used the fortunetelling in order to accomplish the ultimate purpose of Buddhism. So the fortunetelling is a mean of propagation of Buddhism and a intermediary of awakening and salvation.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.478-489
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• 2020

This study presents an analysis of infrasonic signals from two accidental explosions in Gwangyang city, Jeonnam Province, Korea, on December 24, 2019, recorded at 12 infrasound stations located 151-435 km away. Infrasound propagation refracted at an altitude of ~40 km owing to higher stratospheric wind in the NNW direction, resulting in favorable detection at stations in that direction. However, tropospheric phases were observed at stations located in the NE and E directions from the explosion site because of the strong west wind jet formed at ~10 km. The transmission losses on the propagation path were calculated using the effective sound velocity structure and parabolic equation modeling. Based on the losses, the observed signal amplitudes were corrected, and overpressures were estimated at the reference distance. From the overpressures, the source energy was evaluated through the overpressure-explosive charge relationship. The two explosions were found to have energies equivalent to 14 and 65 kg TNT, respectively. At the first explosion, a flying fragment forced by an explosive shock wave was observed in the air. The energy causing the flying fragment was estimated to be equivalent to 49 kg or less of TNT, obtained from the relationship between the fragment motion and overpressure. Our infrasound propagation modeling is available to constrain the source energy for remote explosions. To enhance the confidence in energy estimations, further studies are required to reflect the uncertainty of the atmospheric structure models on the estimations and to verify the relationships by various ground truth explosions.