• Smart Structures and Systems
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• 제33권3호
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• 전자공학회논문지
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• 제50권12호
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• pp.277-284
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• 2013

뇌파의 위상동기 특성의 해석은 뇌기능 정보 처리 네트웍의 이해에 있어서 중요한 요소이다. 본 연구에서는 위상동기 성능을 분석하기 위하여 시간 국부화 기능을 갖는 웨이블렛 변환방법, 힐버트변환 방법, 복소복조방법을 백색잡음혼입한 뇌파 중심주파수 대역신호에 합성한 모의 데이터와 실제 뇌유발전위에 적용하여 위상옵셋, 위상변화 시점, 대역간 상호간섭을 분석하였다. 모의데이터와 실제 데이터에 대하여 웨이블렛 변환방법이 ${\delta}$, ${\theta}$, ${\alpha}$ 대역신호 분할과 위상동기 성능 분석에 가장 우수 하였으며, 복소복조 방식은 큰 혼입잡음 환경에서는 사용에 한계점이 있었으나 위상변화 시점의 검출에는 좋은 성능을 보였다. 실제 데이터에 대하여 추정한 위상변화시점과 위상옵셋은 복소복조방법이 가장 좋은 성능을 보였다. 실험 관측을 통하여 뇌파의 위상동기 현상의 해석에 웨이블렛 방식이 자장 적절하였으며, ${\delta}$ 대역 이상신호에 대한 위상동기 응용에는 복소복조 방법도 사용될 수 있을 것이다. 특히 웨이블렛 방식과 복조복조방식은 간접적으로 위상변화 시점을 검출하여 뇌기능의 변화시점 추정에 응용될 수 있을 것이다.

• 생물정신의학
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• 제8권2호
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• pp.251-257
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• 2001

Backgrounds : Gamma band oscillatory activity is considered to be related to cognitive functions and illustrates that the concept of event-related oscillations bridges the gap between single neurons and neural assemblies. An event-related gamma oscillation is the time-locked responses of specific frequency, and can be identified by computing the amplitude frequency characteristics of the averaged event-related potentials(ERPs) after stimulation. Objectives : We purposed to present experimental paradigm to investigate ${\gamma}$-band oscillation activities from the recording of ERPs by using auditory oddball paradigm and investigate the difference of ${\gamma}$-band activity between schizophrenia and normal controls. Methods : The ERPs resulting from auditory stimuli with oddball paradigm in a group of schizophrenics(n=11), and also a group of age-, sex-, and handedness matched normal controls, were recorded by 128 channel EEG. The ${\gamma}$-band oscillatory activities were calculated by using time-frequency wavelet decomposition of the signal between 20 and 80Hz. The ${\gamma}$-band oscillatory activities of both groups were compared by t-test. Results : The ${\gamma}$-band oscillatory of the leads Fz, Cz, and Pz of both groups were represented well in the time-frequency maps. Significant increases of the ${\gamma}$-band activity in normal controls compared with schizophrenics were observed around 160 msec, 350 msec, and 800 msec after stimulation. Conclusions : Our results suggested that the increment in ${\gamma}$-band oscillatory activity during cognitive operations and decreased ${\gamma}$-band activity in schizophrenics may be associated with the cognitive dysfunctions and the pathophysiology of the schizophrenia.