• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.338-343
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• 2009

Tomography is the imaging method of cross sectional area using multi beam signals and is mainly applied to the medical diagnosis to acquire the image of the inside human body. This method is pretty meaningful in nondestructive evaluation field since the imaging of the inspection region can enhance the comprehension of the inspector. Recently, much attention has been paid to the guided wave for the diagnosis of platelike structures. So, in this work, a study on the imaging of the damage location in a plate was carried out on the basis of computer aided analysis of guided waves and tomographic imaging. To this end, boundary element method was employed to analyze the effect of the damage in plate on the propagation of the guided waves and the analytic results were applied to the tomographic imaging method to identify the damage location. Consequently, it was shown that the number of sensors heavily affect the inspection performance of the damage location.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.81-81
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• 2011

현재 국내에서 사용하고 있는 교량구조물의 성능평가방법으로는 크게 공용하중에 대한 내하율을 구하기 위하여 허용응력개념이나 강도설계 개념을 적용한 내하력 평가 기법이 사용되고 있다. 그러나 위의 방법들은 일반적으로 공용연수의 경과에 따른 재료 및 구조적 성능의 손실과 여러 가지 하중 및 환경적 요인들의 불확실성으로 인하여 발생하는 손상 및 열화를 반영하기 어렵다. 그리고 제원 및 재료물성치의 불확실성에 대한 기존 설계 자료의 DB 부족으로 기존의 평가방법에서는 이러한 시간의 경과에 따른 성능저하를 정확히 산정할 수 없어 이론상의 값과 실제 구조물과의 차이로 인한 불확실성이 존재 한다. 이에 본 연구에서는 공용년수 경과에 따른 시설물의 재료 구조적인 성능 및 거동분석 수행, 신뢰성 해석 수행을 바탕으로 교량 안전성 평가의 합리성 및 현실성을 제고하며, 구조 신뢰성 해석을 수행함으로써 실제 구조물의 강도 한계상태에 대한 파괴확률을 산정하고 그에 대응하는 위험도를 평가함으로써 안전성 검토를 수행하였다. 본 실험을 통해 1. 재료 강도, 부재 제원, 긴장력, 작용하중 등에 있어 설계 시 가정과 실제 사용 환경 사이의 변동성이 존재한다는 것을 알 수 있었으며, 2. 연구 수행 결과 일반 정밀진단 및 해석에서는 얻을 수 없는 다양하고 중요한 결과를 산출할 수 있었으며 이러한 연구 결과를 바탕으로 개선된 성능평가 기법이 제안 될 수 있음을 알 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.215-215
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• 2015

SWG(Smart Water Grid)는 한정적인 수자원을 효율적으로 이용하고 사용자 입맛에 맞춰 물을 안정적으로 공급하고자 하는 양방향 개념으로 수자원 분야에서 각광받는 실정이다. SWG의 성능을 평가하는 연구사례는 많지 않으며 현재 국내의 경우 실증단지를 구축하고 있는 단계이다. 그러므로 성능평가를 수행하기 위한 실측자료가 부재하므로 성능평가 지표선정에 활용 할 수 있는 자료가 제한적이다. 따라서 미국의 Leed, 영국의 BREEAM, 일본의 CASBEE, 호주의 Green Star의 인증 기준에서 수자원에 대한 항목과 기존의 IWA, World Bank, AWWA, JWWA, OFWAT 같은 해외 상수도 수행능 지표와 상수도 및 정수장 기술진단에서 제시하는 기준을 바탕으로 성능평가지표를 선정 보완하여 Water Facility Index, Smart Index, Optimum System Index로 분류하여 각 세부의 성능평가지표를 선정하였다. 선정된 지표는 AHP기법를 활용하여 1-4단계에 걸쳐 계층화를 실시하고 Bottom-up방식으로 4단계에서부터 1단계로 평가를 진행하도록 구성했다. 4단계에서는 계층 별 쌍대비교 결과를 바탕으로 중요도에 따른 가중치를 부여하고 차등 점수를 적용하며, 이는 3단계에서 필수적으로 만족해야하는 지표들을 설정하고 평가하기 위해 사용된다. 3단계에서는 지표평가 후 "보통" 이상의 점수를 획득해야 2단계로 진행할 수 있고, 2단계에서는 3가지 큰 지표를 설정 후 총점 기준 60%이상의 최소득점 기준을 만족해야 1단계로 진행할 수 있도록 구성했다. 이러한 절차를 바탕으로 마지막 1단계에서는 100점을 만점으로 하는 6단계의 최종성능을 평가하는 결과를 제시할 수 있다. 본 연구에서는 국외 성능평가 사례 중 SWG와 관련된 지표를 구성하여 성능평가를 수행할 수 있는 기법을 제안하였다. 향후 지속적인 연구 진행 결과와 실측자료를 수집하여 수정 및 보완이 수행될 예정이다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.73-80
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• 2012

Recently, medical equipments are developed and used for diagnosis or studies. In addition, demand of techniques which automatically deal with three dimensional medical images obtained from the medical equipments is growing. One of the techniques is automatic bone segmentation which is expected to enhance the diagnosis efficiency of osteoporosis, fracture, and other bone diseases. Although various researches have been proposed to solve it, they are unable to be used in practice since a size of the medical data is large and there are many low contrast boundaries with other tissues. In this paper, we present a fast and accurate automatic framework for bone segmentation based on multi-resolutions. On a low resolution step, a position of the bone is roughly detected using constrained branch and mincut which find the optimal template from the training set. Then, the segmentation and the registration are iteratively conducted on the multiple resolutions. To evaluate the performance of the proposed method, we make an experiment with femur and tibia from 50 test knee magnetic resonance images using 100 training set. The proposed method outperformed the constrained branch and mincut in aspect of segmentation accuracy and implementation time.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• The Journal of the Acoustical Society of Korea
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• v.29 no.6
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• pp.355-364
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• 2010

Considering biological safety, it is very important to measure acoustic power from ultrasonic array probe for diagnostic ultrasound imaging applications. In this paper, to measure acoustic power from each element on array probe for ultrasonic diagnostic equipment, we reconstruct and automate the acoustic power measurement system. The acoustic power from linear, phased and curved array were measured and analyzed. As a result of measurement, the effects caused by directivity of sound beam from curved array were founded. To remove these effects, we developed and applied the correction model. The proposed system is useful to evaluate characteristics of the acoustical output power of array probe.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.1-12
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• 2023

This paper assesses the suitability of existing standards for plastic material models and performance level evaluation methods in seismic performance evaluations of concrete dam piers during Maximum Credible Earthquakes (MCE). Dynamic plastic analysis was conducted to examine the applicability of the plastic material model under various conditions. As a result reveal that when the minimum reinforcement ratio is not met, the average stress-average strain method recommended in current dam seismic performance evaluation guidelines tends to underestimate pier responses compared to the predicted outcomes of dynamic elastic analysis. Consequently, the paper proposes an improvement plan that treats dam piers with an insufficient minimum reinforcement ratio as unreinforced and integrates fracture energy into concrete tensile behavior characteristics for performance level evaluation. Implementing these improvements can lead to more conservative evaluation outcomes compared to current seismic performance evaluation methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1044-1051
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• 2006

System level diagnosis algorithms use the properties of t-diagnosable system where the maximum number of the faults does not exceed 1. The existing diagnosis algorithms have limit when dealing with large fault sets in large multiprocessor systems. Somani and Peleg proposed t/k-diagnosable system to diagnose more faults than t by allowing upper bounded few number of units to be diagnosed incorrectly. In this paper, we propose adaptive hypercube diagnosis algorithm using t/k-diagnosable system. When the number of faults exceeds t, we allow k faults to be diagnosed incorrectly. Simulation shows that the performance of the proposed algorithm is better than Feng's HADA algorithm. We propose new algorithm to reduce test rounds by analyzing the syndrome of RGC-ring obtained in the first step of HADA/IHADA method. The proposed algorithm also gives similar performance compared to HYP-DIAG algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.1-9
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• 2024

This study proposes a multi-scale template matching technique with image pyramids (TMI) to measure structural dynamic displacement using a vision sensor under atmospheric turbulence conditions and evaluates its displacement measurement performance. To evaluate displacement measurement performance according to distance, the three-story shear structure was designed, and an FHD camera was prepared to measure structural response. The initial measurement distance was set at 10m, and increased with an increment of 10m up to 40m. The atmospheric disturbance was generated using a heating plate under indoor illuminance condition, and the image was distorted by the optical turbulence. Through preliminary experiments, the feasibility of displacement measurement of the feature point-based displacement measurement method and the proposed method during atmospheric disturbances were compared and verified, and the verification results showed a low measurement error rate of the proposed method. As a result of evaluating displacement measurement performance in an atmospheric disturbance environment, there was no significant difference in displacement measurement performance for TMI using an artificial target depending on the presence or absence of atmospheric disturbance. However, when natural targets were used, RMSE increased significantly at shooting distances of 20 m or more, showing the operating limitations of the proposed technique. This indicates that the resolution of the natural target decreases as the shooting distance increases, and image distortion due to atmospheric disturbance causes errors in template image estimation, resulting in a high displacement measurement error.