• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2009년도 학술대회 초록집
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• pp.147-154
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• 2009

암석의 속도를 정확히 예측하기 위해서는 속도에 1차적인 영향을 미치는 공극구조의 연구가 필수적이다. 이에 본 연구에서는 고해상도 구조 해석에 가장 많이 사용하고 있는 X선 토모그래피 방법을 이용하여 공극구조를 획득하였다. 그러나 X선 토모그래피 방법의 경우 그 역산과정에서 발생하는 smoothing 효과에 의해 공극구조가 왜곡될 수 있다.이를 간단한 공극구조 생성 방법인 single threshold 방법으로 이분화 할 경우 grain contact 부분이 명확히 표현되지 않아 입자의 접촉면적에 좌우되는 속도의 경우 많은 오차를 야기한다. 또한 grain contact의 정확한 기술을 위해서는 고해상도 토모그램 획득이 매우 중요하며, 해상도에 따른 속도의 변화양상 또한 정량적 분석이 필요한 부분이다. 이를 위해 본 연구에서는 영상처리 기법을 적용하여 다양한 이분화를 시도하고, 서로 다른 해상도의 토모그램을 이용하여 이들이 속도 계산에 미치는 영향을 분석하였다. 다양한 영상처리 기법을 적용한 결과 single threshold 방법으로 이분화 한 결과보다 정확한 접촉면적을 보여주는 이분화 결과를 얻을 수 있었지만 실제 계산된 속도에서는 그 향상 정도가 미미하였다. 고해상도 토모그램을 이용한 경우에는 입자의 grain contact이 명확하게 표현되었고, 속도 또한 상당히 향상된 결과를 보여주었다. 결론적으로 디지털 공극구조에서 시뮬레이션을 통한 속도 예측의 경우, 입자의 접촉 부분을 정확히 기술 할 수 있는 높은 해상도의 토모그램이 필수적이며, smoothing 효과의 제거 등의 영상처리와 병행된다면 보다 정확한 암석의 속도 예측이 가능할 것으로 판단된다.

• Structural Monitoring and Maintenance
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• 제2권1호
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• 대한전자공학회논문지SP
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• 제44권6호
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• pp.47-53
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• 2007

블록 기반 프레임 레이트 변환 (frame-rate conversion) 또는 필름 떨림 보상 (film judder compensation)을 수행하기 위해서는 참 움직임 벡터(true motion vector)를 찾아야 한다. 이를 위해서 현재 블록의 공간적 및 시간적 상관성을 최대로 하여 시각적으로 덜 부자연스럽게 느끼도록 하는 방법들이 연구되었다. 그러나 기존의 블록단위 절대값 차이의 합 (SAD)만으로는 비정형성 객체의 움직임 에러를 추정할 수 없었다. 본 논문에서는 비정형성 객체가 등속운동을 하는 경우 재귀적으로 기존의 움직임을 유지하도록 하는 방법을 제안하였다. 현재 블록의 등속움직임 추정값을 재귀평균으로 구하였으며 현재 블록 벡터의 신뢰도를 계산하여 원래의 움직임 벡터와 재귀평균 움직임 벡터중에서 가중치를 두도록 하였다. 실험결과 비정형성 등속운동 객체의 움직임을 블록기반으로 추정함을 확인할 수 있었다.

• Fibers and Polymers
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• 제2권3호
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• pp.164-170
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• 2001

Authors studied on the applicability of image analysis technique using a scanner with a CCD (charged coupled deviced) to the evaluation of evenness of nonwovens because it has distinctive features to considerably save time and labor in the analysis compared with other classical methods. As specimens fur the experiment, two different types that are unpatterned and patterned ones were prepared. For the unpatterned specimen, webs were chemically bonded, while for the patterned specimen, webs being thermally calendered with engraved roller. Several webs having various areal densities were prepared and bonded. Coefficient of variation (CV%) was used as a parameter to evaluate the evenness. Scanning conditions could be suitably set up through comparing the total variance to the between-group variance and to the within-group variance, respectively, on the images scanned at the different conditions. The 2D convolution method with smoothing filter kernel was introduced to further filter the noises on the scanned images. After the filtering process, the increase of web areal densities gave an uniform decrease of the CV%. This showed that the scanned image analysis with proper filtering process could be successfully applicable to the evaluation of evenness in nonwovens.

• 전기학회논문지P
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• 제66권4호
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• pp.213-218
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• 2017

The DC electrolytic capacitor for DC-link of power converter is widely used in various power electronic circuits and system application. Its functions include, DC Bus voltage stabilization, conduction of ripple current due to switching events, voltage smoothing, etc. Unfortunately, DC electrolytic capacitors are some of the weakest components in power electronics converters. Many papers have proposed different algorithms or diagnosis method to determinate the ESR and tan ${\delta}$ capacitance C for fault alarm system of the electrolytic capacitor. However, both ESR vary with frequency and temperature. Accurate knowledge of both parameters at the capacitors operating conditions is essential to achieve the best reference data of fault alarm. According to parameter analysis, the capacitance increases with temperature and the initial ESR decreases. Higher frequencies make the reference ESR with the initial ESRo value to decrease. Analysis results show that the proposed DC Bus electrolytic capacitor reference ESR model setting technique can be applied to advanced reference signal of capacitor diagnosis systems successfully.

• 한국통신학회논문지
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• 제31권12C호
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• pp.1194-1200
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• 2006

본 논문에서는 국부 통계 특성을 이용한 적응 MAP 방식의 고해상도 영상 복원 알고리즘에 대해 제안한다. 고해상도 원 영상의 윤곽선을 보존하기 위해 저해상도 영상의 국부 특성을 이용하여 시각함수를 정의하였고, MAP(Maximum A Posteriori) 추정 방식을 이용하여 국부적인 열화 정도(smoothness)를 조절하였다. 또한 가중치가 부여된 함수를 이용하여 원 고해상도 영상에 가능한 가까운 최적의 해를 찾기 위하여 반복기법을 사용하였으며, 열화 요소는 매 반복 단계마다 부분적으로 복원된 고해상도 영상으로부터 이용하였다. 제안된 방식의 성능을 실험 결과를 통해 확인할 수 있었다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권8호
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• International Journal of Concrete Structures and Materials
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• 제19권1E호
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권3호
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• pp.173-180
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• 2005

본 논문은 스테레오 정합(stereo matching)과 마칭큐브(marching cube)알고리즘을 통합하는 효과적인 알고리즘을 제안한다. 여러 각도에서 획득한 영상에 대해 스테레오 정합 기술을 적용하여 3차원 형상 데이타를 획득하고 카메라 외부 파라미터를 이용하여 결합하였다. 결합된 데이타를 영상 색인을 이용하여 메쉬로 재구성한 다음 각 점에 해당하는 법선벡터를 획득하고 메쉬 평탄화(mesh smooth)의 과정을 거쳐서 데이타를 부드럽게 처리하였다. 본 논문은 3차원 메쉬 재구성에 대한 일련의 과정 및 기술을 서술하였으며, 기존의 마칭큐브 알고리즘에서 생기는 3차원 데이타의 불안정에 대한 문제를 중간 등위면(iso-surface) 알고리즘을 제안하여 개선하였다.

• 콘크리트학회논문집
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• 제17권6호
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.