• Journal of Korean Society of Steel Construction
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• v.17 no.1 s.74
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• pp.1-11
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• 2005

A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally-acquired signals are used to test the NN. The results of this example application suggest that the SAI-based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.

• Journal of Ocean Engineering and Technology
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• v.14 no.1
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• pp.80-87
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• 2000

Despite many combined research efforts outstanding needs exist to develop robust safety-estimation methods for large complex structures. This paper presents a practical damage identification scheme which can be applied to truss structures using only limited modal responses. firstly a theory of pattern recognition (PR) is described. Secondly existing damage-detection algorithms are outlined and a newly-derived algorithms for truss structures. Finally the feasibility of the proposed scheme is evaluated using numerical examples of plane truss structures.

• Journal of the Korean Society of Radiology
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• v.82 no.2
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• pp.359-370
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• 2021

Purpose To analyze the findings and serial changes in chest CT lesions in 123 symptomatic patients with coronavirus disease 2019 (COVID-19). Materials and Methods From February 19 to April 7, 2020, a total of 123 confirmed COVID-19 patients (male, 44; female, 79; mean age, 59.2 ± 18.6) were enrolled in this retrospective study. A total of 234 CT scans were reviewed for the following patterns: acute alveolar insult (AAI) patterns: ground-glass opacity (GGO), crazy-paving appearance, mixed pattern, and consolidation; organizing pneumonia (OP) patterns: perilobular patterns, band opacity, curvilinear opacity, reversed halo opacity, and small nodular consolidation; resolving patterns: pure GGO, remnant curvilinear, small nodular consolidation, and serial changes of lung abnormalities. We compared the proportions of AAI pattern, OP pattern, or resolving pattern with time progression and analyzed the association between the patterns and disease severity using Pearson chi-square and Fisher's exact test. Results Predominant CT patterns were AAI pattern (87%) in the early hospital period group (0-10 days, after the onset of symptoms), OP pattern (45.7%) in the later hospital period group (after 10 days), and resolving pattern in discharge and follow-up group (47.2% and 84.8%, respectively). The difference in the proportions of predominant CT patterns with time progression was statistically significant (p < 0.001, Pearson's chi-square test). No statistically significant association was observed between the patterns and disease severity (p = 0.055, Fisher's exact test). No fibrous changes in the lesions were observed on follow-up CT scans. Conclusion The serial CT scans of COVID-19 patients showed the spectrum of COVID pneumonia CT manifestations as different phases of lung injury and repair.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.747-754
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• 2014

In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.77-84
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• 2020

In this paper, we developed a damage evaluation technique that can determine the damage location of a long-sized structure such as a cable-stayed bridge, and verified the performance of the developed technique through experiments. The damage assessment method aims to extract data that can evaluate the damage of the structure without the undamage data and can determine the damage location only by analyzing the response data of the structure. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To evaluate the performance of the developed technique experimentally, cable damage experiments were conducted on model cable-stayed bridges. As a result, the damage assessment method considering variability automatically outputs the damageless data according to external force, and it is confirmed that the performance of extracting information that can determine the damage location of the cable through the analysis of the outputted damageless data and the measured damage data is shown.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.351-359
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• 2011

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.981-982
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• 2008

• The Journal of Korean Physical Therapy
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• v.16 no.2
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• pp.128-139
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• 2004

말초신경은 외상이나 질병 등 여러 가지 원인으로 손상되기 쉬우며, 손상의 정도가 심하거나 치료가 지연되는 경우에는 심각한 기능 소실을 초래할 수 있다. 본 연구에서는 수영이 말초신경손상후 운동기능의 회복과 뇌유인성 신경영양인자 (brain-derived neurotrophic factor, BDNF) mRNA의 발현에 미치는 효과를 알아보기 위하여, 흰쥐 좌골신경에 압박 손상을 가하고 수영을 적용한 후 보행궤적분석 (walking track analysis)과 역전사연쇄반응 (reverse transcription-polymerase chain reaction, RT-PCR)을 실시하였다. 그 결과, 좌골신경 압박손상된 쥐는 특징적인 보행패턴을 나타내어 좌골신경기능지수 (sciatic function index, SFI)가 현저히 낮아졌으며, BDNF mRNA의 발현이 증가하였다. 좌골신경 압박 손상후 수영을 한 쥐에서는 SFI가 현저히 향상되었으며, BDNF mRNA의 발현은 억제되었다. 이러한 결과는 말초신경손상후 수영이 BDNF mRNA의 발현을 조절함으로써 기능 회복을 촉진시키는 효과적인 치료방법이 될 수 있음을 제안하고 있다.

• Journal of Navigation and Port Research
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• v.36 no.3
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• pp.163-168
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• 2012

Recently, people's desire for the waterfront space has been increasing, and more people want to spend their leisure time close to the water. This paper proposes a damage detection technique using the static strain for the floating structure. An existing damage index, in which the modal strain energy was utilized to identify possible location of damage, is expanded to apply the static strain. The new damage index is expressed in terms of the static strains of undamaged and damaged structures. After calculating damage index, the possible damage locations in the structure are determined by the pattern recognition technique. The accuracy and feasibility of the proposed method is demonstrated by using experimental strain data from a scale model of floating structure.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.3
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• pp.134-141
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• 2011

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.