• 한국구조물진단유지관리공학회 논문집
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• 제15권2호
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• pp.70-78
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• 2011

본 논문은 변형률 계측데이터를 사용하는 신뢰성 및 정확성을 증진된 BWIM(Bridge Weigh-In-Motion) 알고리즘을 개발하고, 이를 교량에 대한 다양한 실험을 통해 검증하고자 하는 것이다. 본 논문에서는 밀도추정함수와 평균보정계수를 이용한 BWIM 알고리즘을 제시한다. 밀도추정함수는 다축하중을 추정할 때 신뢰할 수 있게 적용할 수 있음을 입증하였으며, 평균보정계수는 이론 계산된 모멘트와 계측된 변형률에서 계산한 모멘트 사이의 전반적인 오차를 최소화하기 위해 적용된다. 개발된 알고리즘은 수치예제, 실내모형실험 그리고 다주형 합성교량에 대한 현장실험을 통해 성공적으로 검증하였다.

• 한국구조물진단유지관리공학회 논문집
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• 제6권2호
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• pp.121-126
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• 2002

Due to few earthquakes in our country, one generally has thought to be safe from earthquakes. However, severe earthquakes occurred in Dangsan and Hyogohyeon which one had regarded as the zone that had not been risky for earthquakes, so that so many people died and a lot of buildings and bridges were destroyed. This event surprised our country and we undertook preparation for earthquakes on the full scale. The concept of seismic design was induced in the country which was poor in it for the scarcity of recognition and insufficiency of funds. Recently, many specialists are enforcing the provisions of seismic design. Therefore, this study introduces the method which combines PC-LEADeR( design program for L.R.B.) with LUSAS(linear elastic analysis) and performs the seismic isolation design more elaborately and simply. It verifies the propriety of that method, and it also examine the factors that affect the response of the bridges. Seismic isolation design for bridge using L.R.B. provides both economical efficiency and superior seismic performance. Second, the results between by the method proposed and by time history analysis have 20% error at the maximum. That is, the method proposed very appropriate.

• 제어로봇시스템학회논문지
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• 제20권10호
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• pp.1044-1050
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• 2014

Robots used for pipe inspection have been studied for a long time and many mobile mechanisms have been proposed to achieve inspection tasks within pipelines. Localization is an important factor for an inpipe robot to perform successful autonomous operation. However, sensors such as GPS and beacons cannot be used because of the unique characteristics of inpipe conditions. In this paper, an inpipe localization algorithm based on elbow detection is presented. By processing the projected marker images of laser pointers and the attitude and heading data from an IMU, the odometer module of the robot determines whether the robot is within a straight pipe or an elbow and minimizes the integration error in the orientation. In addition, an off-line positioning algorithm has been performed with forward and backward estimation and Procrustes analysis. The experimental environment has consisted of several straight pipes and elbows, and a map of the pipeline has been constructed as the result.

• 한국안전학회지
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• 제34권1호
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• pp.45-52
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• 2019

Accident analysis models were developed to improve the construction site safety and case studies was conducted. In 2016, 86% of fatality accidents occurred due to simple unsafe acts. Structure related accidents are less frequent than the non structure related causes, but the number of casualties per accident is two times higher than non structure one. In the view of risk perception, efforts should be given to reduce accidents caused by low frequency - high consequence structure related causes. In case of structure related accident, structural safety inspection and management (including quality), ground condition management / inspection technology, and provision of risk information delivery system in case of non structure related accident were proposed as a solution. In analysis of relationship between safety related stakeholder, the main problem were the lack of knowledge of controller and player, loss of control due to duplicated controls, lack of communication system of risk information, and relative position error of controller and player.

• 한국기계가공학회지
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• 제19권11호
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• pp.87-93
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• 2020

A key component of aviation safety is to eliminate the errors in commercial aircraft air data systems to ensure stable aviation operation. Although the technical aspects such as the maintenance and inspection play a pertinent role, human errors are expected to have a similar or even larger influence on the aviation safety. Aviation maintenance and inspection tasks are often performed by a complex organization, in which individuals perform a variety of tasks in an environment involving time pressure, sparse feedback, and complex conditions. These situational characteristics, combined with the general tendency of human error, may lead to various types of errors, which may have critical consequences such as accidents and loss of life. For instance, if an amber message "IAS DISAGREE" is displayed on the primary flight display while the aircraft is rolling on the runway to takeoff, the crew immediately performs a rejected takeoff operation and troubleshoots the air data system. This paper proposes alternative approaches to address the occurrence of defects due to the human factors involved in the practical processes of the air data system of commercial aircraft.

• Smart Structures and Systems
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• 제29권1호
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Smart Structures and Systems
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• 제32권2호
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

• 핵의학기술
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• 제27권1호
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• pp.11-22
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• 2023

Bone densitometry is a disease in which bones are easily broken due to metabolic bone disease, and DXA is used as a clinical standard test. Although DXA is a good method with good accuracy and reproducibility, it is frequently subject to test errors in testing and result analysis and analysis. Therefore, it is important to recognize the error issues that radiologists should basically be aware of when performing bone density tests, prevent erroneous diagnoses and treatments resulting from the results, and reduce the unnecessary costs associated with them. aim. The inspection must be carried out if the quality control of the equipment is basically continuously performed well before the inspection. Before starting the examination, the patient's age, sex, race, weight, pregnancy status, and any foreign objects that can be removed should be checked, and the examination should be performed in the correct posture. In addition, it is important to analyze results consistently. Radiologists, who play the most important role in ensuring accurate examinations, need to be aware of the potential for errors in advance and develop the ability to deal with the potential for errors in each examination. For that reason, regular education is considered essential.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• 한국압력기기공학회 논문집
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• 제19권2호
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.