• Smart Structures and Systems
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• v.11 no.3
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• pp.261-282
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• 2013

Structural health monitoring (SHM) is an application area of Wireless Sensor Networks (WSNs) which usually needs high data communication rate to transfer a large amount of monitoring data. Traditional sink node can only process data from one communication channel at the same time because of the single radio chip structure. The sink node constitutes a bottleneck for constructing a high data rate SHM application giving rise to a long data transfer time. Multi-channel communication has been proved to be an efficient method to improve the data throughput by enabling parallel transmissions among different frequency channels. This paper proposes an 8-radio integrated sink node design method based on Field Programmable Gate Array (FPGA) and the time synchronization mechanism for the multi-channel network based on the proposed sink node. Three experiments have been performed to evaluate the data transfer ability of the developed multi-radio sink node and the performance of the time synchronization mechanism. A high data throughput of 1020Kbps of the developed sink node has been proved by experiments using IEEE.805.15.4.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.637-642
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• 1998

This study was performed to develop effective management system of concrete structures in Nuclear Power Plants. This D/B system includes three kinds of data : 1)visual inspection data(cracking, spalling, etc.) 2) durability data carbonation, chloride attack, etc. 3) in-service inspection data(prestressing force. material properties, etc. ) By using the life management D/B System, the field engineers can easily acquire the information about the various inspection data. repair and accidental histories of structures. This system, will contribute to the efficient life management of concrete structures.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.283-290
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• 2009

The rank/select data structure is a basic tool of succinct representations for several data structures such as trees, graphs and text indexes. For a given string sequence, it is used to answer the occurrence of characters up to a certain position. In previous studies, theoretical rank/select data structures were proposed, but they didn't support practical operational time and space. In this paper, we propose a simple solution for implementing rank/select data structures efficiently. According to experiments, our methods without complex encodings achieve nH$_0$ + O(n) bits of theoretical size and perform rank/select operations faster than the original HSS data structure.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.74-81
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• 1995

Several non-manifold data structures have been compared, which are radial-edge data structure, partial-face data structure, vertex-based data structure, and Yamaguchi's data structrue. All the entities in the data structures are classified into common entities and special entities. The entities are also classified as model entities, primitive entities bounding entities, and coupling entities. The four data structures for nonmanifold solid modelers are compared in terms of accessing efficiency, storage requirements, and inclusion of circulation. The results of comparison will serve as the basis to develope a nonmanifold modeler.

• Smart Structures and Systems
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• v.23 no.1
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• pp.61-69
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• 2019

Offshore structures are generally exposed to harsh environments such as strong tidal currents and wind loadings. Monitoring the structural soundness and integrity of offshore structures is crucial to prevent catastrophic collapses and to prolong their lifetime; however, it is intrinsically challenging because of the difficulties in accessing the critical structural members that are located under water for installing and repairing sensors and data acquisition systems. Virtual sensing technologies have the potential to alleviate such difficulties by estimating the unmeasured structural responses at the desired locations using other measured responses. Despite the usefulness of virtual sensing, its performance and applicability to the structural health monitoring of offshore structures have not been fully studied to date. This study investigates the use of virtual sensing of offshore structures. A Kalman filter based virtual sensing algorithm is developed to estimate responses at the location of interest. Further, this algorithm performs a multi-sensor data fusion to improve the estimation accuracy under non-stationary tidal loading. Numerical analysis and laboratory experiments are conducted to verify the performance of the virtual sensing strategy using a bottom-fixed offshore structural model. Numerical and experimental results show that the unmeasured responses can be reasonably recovered from the measured responses.

• Computers and Concrete
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• v.31 no.3
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• pp.241-252
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• 2023

Corrosion of rebar is one of the major deteriorating mechanisms that affect the durability of reinforced concrete (RC) structures. The increase in CO₂ concentration in the atmosphere leads to early carbonation and deterioration due to corrosion in RC structures. In the present study, an attempt has been made to modify the existing carbonation depth prediction empirical model. The modified empirical model is verified from the carbonation data collected from selected RC structures of CSIR-SERC campus, Chennai and carbonation data available from the reported literature on in-situ RC structures. Attempt also made to study the carbonation depth in the laboratory specimens using oxygen permeability index (OPI) test. The carbonation depth measured from RC structures and laboratory specimens are compared with estimated carbonation depth obtained from OPI test data. The modified empirical model shows good correlation with measured carbonation depth from the identified RC structures and the reported RC structures from the literature. The carbonation depth estimated from OPI values for both in-situ and laboratory specimens show lesser percentage of error compared to measured carbonation depth. From the present investigation it can be said that the OPI test is the suitable test method for both new and existing RC structures and laboratory RC specimens.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.663-670
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• 2003

Urban tunnelling need to consider not only the stability of tunnel itself but also the ground movement regarding adjacent structures. This paper present 3-D behavior of adjacent structures due to tunnelling induced ground movements by means of field measuring data and nonlinear FEM tunnel analysis. The results of the analytical methods from Mohr-Coulomb model are compared with the site measurement data obtained during the twin tunnel construction. It was found that the location and stiffness of the structure influence greatly the shape and pattern of settlement trough. The settlement trough for Greenfield condition was different from the trough for existing adjacent structures. Therefore the load and stiffness of adjacent structures should be taken into account for the stability analysis of the structures.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.189-196
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• 2014

Though the plagiarism is illegal and should be avoided, it still occurs frequently. Particularly, the plagiarism of source codes is more frequently committed than others since it is much easier to copy them because of their digital nature. To prevent code plagiarism, there have been reported a variety of studies. However, previous studies for plagiarism detection techniques on source codes do not consider the data structures although a source code consists both of data structures and algorithms. In this paper, a plagiarism detection technique for source codes considering data structures is proposed. Specifically, the data structures of two source codes are represented as sets of trees and compared with each other using Hungarian Method. To show the usefulness of this technique, an experiment has been performed on 126 source codes submitted as homework results in an object-oriented programming course. When both the data structures and the algorithms of the source codes are considered, the precision and the F-measure score are improved 22.6% and 19.3%, respectively, than those of the case where only the algorithms are considered.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.