• Smart Structures and Systems
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• 제11권3호
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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.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1998년도 가을 학술발표대회 논문집(III)
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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.

• 한국정보과학회논문지:시스템및이론
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• 제36권4호
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• pp.283-290
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• 2009

Rank/select 자료구조는 트리, 그래프, 문자열 인덱스 등의 다양한 자료구조를 간결하게 표현하는 기본 도구이다. Rank/select 자료구조는 주어진 문자열에 어느 위치까지 나타난 문자 개수를 세는 연산을 처리한다. 효율적인 rank/select 자료구조를 위해 이론적인 압축 방식들이 제안되었으나, 실제 구현에 있어 연산 시간 및 저장 공간의 효율을 보장할 수 없었다. 본 논문은 간단한 방법으로 이론적인 압축 크기를 보장하면서 연산 시간도 효율적인 rank/select 자료구조 구현 방법을 제시한다. 본 논문의 실험을 통해, 복잡한 인코딩 방법 없이도 이론적인 nH$_0$ + O(n) 비트 크기에 근접하면서 기존의 HSS 자료구조보다 빠른 rank/select 연산을 지원하는 구현 방법임을 보인다.

• 한국정밀공학회지
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• 제12권11호
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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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• 제23권1호
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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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• 제31권3호
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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.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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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.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제3권6호
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• pp.189-196
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• 2014

표절은 불법이고 피해야 하지만 여전히 빈번하게 발생하고 있다. 특히, 소스코드 표절은 그 특성상 복사가 용이해 다른 저작물보다 더 빈번히 발생한다. 코드 표절을 방지하기 위한 다양한 연구가 있었다. 하지만 앞서 연구된 소스코드 표절 검사 기법을 살펴보면 프로그램이 알고리즘과 데이터 구조로 구성됨에도 불구하고 데이터 구조는 전혀 고려하지 않고 있다. 이 논문에서는 데이터 구조를 고려한 소스코드 표절 검사 기법을 제안한다. 구체적으로 말해서 두 소스코드의 데이터 구조를 트리 집합으로 나타내고, 헝가리안 메소드를 사용해 비교한다. 제안하는 기법의 효용성을 보이기 위해 객체지향 교과목에서 과제 답안으로 제출한 126개의 소스코드를 대상으로 실험하였다. 실험 결과 데이터 구조와 알고리즘을 모두 고려했을 때, 알고리즘만 고려한 경우보다 정확률과 F-measure가 각각 22.6%, 19.3% 향상됨을 보였다.

• Structural Monitoring and Maintenance
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• 제7권4호
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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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• 제29권1호
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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.