• 한국방재학회:학술대회논문집
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• 한국방재학회 2011년도 정기 학술발표대회
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• pp.39-39
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• 2011

This study presents an effective vision-based system for cable bridge damage detection. In theory, cable bridges need to be inspected the outer as well as the inner part. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs(MLTM) was initiated focusing on the damage detection of cable system. In this study, only the surface damage detection algorithm based on a vision-based system will be focused on, an overview of the vision-based cable damage detection is given in Fig. 1. Basically, the algorithm combines the image enhancement technique with principal component analysis(PCA) to detect damage on cable surfaces. In more detail, the input image from a camera is processed with image enhancement technique to improve image quality, and then it is projected into PCA sub-space. Finally, the Mahalanobis square distance is used for pattern recognition. The algorithm was verified through laboratory tests on three types of cable surface. The algorithm gave very good results, and the next step of this study is to implement the algorithm for real cable bridges.

• 한국학교ㆍ지역보건교육학회지
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• 제7권
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• pp.51-58
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• 2006

Background & Objectives: This study investigates the recognition on the necessity of the use of radiation for both college students who are considered that they have a high knowledge level in radiation and proposes basic materials to change the recognition of the use of radiation. Also, the investigation was applied to average people who showed the most negative attitudes on radiation. Methods: A questionnaire was applied to 600 college students for five days from October 10 to 15, 2005 and used in statistical analysis. Results: The average value obtained in the recognition of the use of radiation was 76.60 points in which male respondents who were majored in natural science, health, and engineering department and respondents who have experienced radiation related education, radiation diagnosis, and radiation treatment demonstrated higher levels. Also, the average value obtained in the recognition of the radiation damage was 71.66 points in which respondents who were majored in natural sciences, humanities, engineering, and health department showed higher levels than that of respondents who were majored in art and physical department. Groups that exhibited higher recognition levels in the necessity of the use of radiation were male respondents and respondents who were majored in natural science, humanities, and health department and have experienced radiation diagnosis and radiation treatment. In the results of the correlation analysis on the necessity of the use of radiation and recognition of radiation damages, the recognition of radiation damages was presented as negative attitudes in the case of the higher recognition level in the necessity of the use of radiation. Conclusions: Regarding the frequency of the use of radiation in Korea, a 80.9% of university students who showed a high education level had no experiences in radiation related education. Although they showed a relatively high level of 76.6 points in the recognition level of the necessity of the use of radiation, the negative attitude on the radiation damage was also presented as a high level of 71.7 points. Because the providing chance of radiation related information was limited as compared to the atomic power used in Korea and dependancy of the use of radiation, it is necessary to provide the basic information related in the use of radiation to the public. In addition, various investigations on the use of radiation and such negative attitudes are required in future for the public. Also, the correct information of the radiation safety should be delivered to the public.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• 한국항해항만학회지
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• 제36권3호
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• pp.163-168
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• 2012

최근 물 가까이에서 생활하고 여가를 보낼 수 있는 친수공간에 대한 욕구가 증가하면서 플로팅 구조물에 대한 관심이 커져가고 있다. 이에 본 연구에서는 정적 변형률을 이용한 플로팅 구조물의 손상탐지기법을 제안하였다. 손상을 탐지하기 위한 손상지수는 기존의 모달 변형에너지를 이용한 손상지수 법을 변형률을 적용할 수 있도록 확장하여 손상 전과 손상 후의 변형률로 나타내었으며, 손상지수 계산 후 손상부위를 결정하는 손상탐지는 패턴인식을 이용하였다. 제안된 이론의 정확성과 타당성은 플로팅 구조물의 축소모형을 제작하고 계측된 변형률 데이터에 적용하여 검증하였다.

• Structural Engineering and Mechanics
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• 제48권3호
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• pp.291-308
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• 2013

Structural health monitoring is crucial to maintain the structural performance safely. Moreover, the Kullback-Leibler divergence (KLD) is applied usually to asset the similarity between different probability density functions in the pattern recognition. In this study, the KLD is employed to detect the damage. However the asymmetry of the KLD is a shortcoming for the damage detection, to overcoming this shortcoming, two other divergences and one statistic distribution are proposed. Then the damage identification by the KLD and its three descriptions from the symmetric point of view is investigated. In order to improve the reliability and accuracy of the four divergences, the gapped smoothing method (GSM) is adopted. On the basis of the damage index approach, the new damage index (DI) for detect damage more accurately based on the four divergences is developed. In the last, the grey relational coefficient and hypothesis test (GRCHT) is utilized to obtain the more precise damage identification results. Finally, a clear remarkable improvement can be observed. To demonstrate the feasibility and accuracy of the proposed method, examples of an isotropic beam with different damage scenarios are employed so as to check the present approaches numerically. The final results show that the developed approach successfully located the damaged region in all cases effect and accurately.

• Structural Engineering and Mechanics
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• 제64권6권
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• pp.803-817
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• 2017

Structural health monitoring has increasingly been a focus within the civil engineering research community over the last few decades. With increasing application of sensor networks in large structures and infrastructure systems, effective use and development of robust algorithms to analyze large volumes of data and to extract the desired features has become a challenging problem. In this paper, we grasp some precautions and key points of the signal processing approach, wavelet, establish a relative reliable framework, and analyze three problems that require attention when applying wavelet based damage detection approach. The cases studies how to use optimal scales for extracting mode shapes and modal curvatures in a reinforced concrete beam and how to effectively identify damages using maximum curves of wavelet coefficient differences. Moreover, how to make a recognition based on the wavelet multi-resolution analysis, wavelet packet energy, and fuzzy sets is a meaningful topic that has been addressed in this work. The relative systematic work that compasses algorithms, structures and evaluation paves a way to a framework regarding effective structural health monitoring, orientation, decision and action.

• 정보처리학회논문지B
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• 제18B권5호
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• pp.271-278
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• 2011

최근 급증하는 기상이변 및 기후온난화 현상은 풍수로 인한 피해를 더욱 가속시키고 있어 풍수해 발생가능성을 미리 예측하여 선제적으로 대응할 방안 마련이 필요하다. 재난 재해의 위험성 분석은 주로 확률 통계기법에 기반한 수식모델 연구가 주류를 이루고 있고 소방방재청 국립방재연구소에서 구축한 태풍위원회 재해정보시스템(TCDIS: Typhoon Committee Disaster Information System) 또한 지역별 풍수해 위험성 분석에 확률모델을 활용하고 있다. 본 논문에서는 경험적 패턴인식에 탁월한 성능을 가진 신경망 알고리즘을 활용하여 개발한 풍수해 예측모델을 소개하고 이 모델과 TCDIS의 KDF 확률밀도함수를 이용한 풍수해 예측모델의 성능 비교 결과를 제시하여 기존 TCDIS의 위험성 분석기능에 신경망 모델을 적용함으로써 시스템의 강건성과 예측 정확도 향상이 가능함을 보이고자 한다.

• 한국구조물진단유지관리공학회 논문집
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• 제15권3호
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• pp.134-141
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• 2011

최근 사회 기반 시설물에서 구조물의 안전성 및 적정 성능 수준을 확보하기 위하여 구조물의 결함 빛 노후화에 의한 성능 저하 등을 상시적으로 모니터링하기 위한 관심이 높아지고 있다. 이 중 배관 구조물은 국가 주요 자원의 수송을 책임지는 핵심 사회 기반 시설물임에도 불구하고 지중에 매립된다는 위치적 특성 상 상시적으로 구조물의 상태를 모니터링하기는 매우 어렵다. 또한 배관 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능하다. 따라서 본 연구에서는 이러한 복합 손상을 효율적으로 진단하기 위하여 압전센서를 이용한 자가 계측 회로 기반의 유도 초음파 계측 시스템을 복합 손상 진단에 적용하였다. 유도 초음파 자가 계측으로부터 특정 중심 주파수에 해당하는 구조물의 웨이블렛 응답을 계측한다. 복합 손상을 유형별로 분류하기 위하여 유도 초음파 계측으로부터 추출한 특성을 이용하여 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법에 적용한다. 제안된 기법의 적용성 검토를 위하여 배관 구조물에 인위적으로 다중 손상을 생성시켜 시험을 수행하였다.

• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.797-802
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• 2016

This paper proposes a noble voice recognition method based on an adaptive MFCC and deep learning for embedded systems. To enhance the recognition ratio of the proposed voice recognizer, ambient noise mixed into the voice signal has to be eliminated. However, noise filtering processes, which may damage voice data, diminishes the recognition ratio. In this paper, a filter has been designed for the frequency range within a voice signal, and imposed weights are used to reduce data deterioration. In addition, a deep learning algorithm, which does not require a database in the recognition algorithm, has been adapted for embedded systems, which inherently require small amounts of memory. The experimental results suggest that the proposed deep learning algorithm and HMM voice recognizer, utilizing the proposed adaptive MFCC algorithm, perform better than conventional MFCC algorithms in its recognition ratio within a noisy environment.