• Steel and Composite Structures
• /
• v.29 no.6
• /
• pp.703-716
• /
• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.17 no.1
• /
• pp.77-85
• /
• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Journal of Advanced Navigation Technology
• /
• v.20 no.3
• /
• pp.210-217
• /
• 2016

Damage caused by FOD which is a foreign substance at the movement area in airports around the world has reached 200 million every year. In 2000, the casualties occurred 133 people at charles de gaulle airport due to FOD. The occurrence of damage by FOD has continuously influenced in domestic also it makes equipment repair indirectly or directly. Accordingly, One of the solutions to the problem is the development of FOD automatic detection system. That is ongoing for plane movement area in airport. As the analyzed result, the military airport prefered mobile type and the civil airport prefered fixed type due to the characteristics of the operating type. In this paper, we analyzed the minimum performance specifications meeting the domestic requirements by investigating military and private FOD detection systems.

• Journal of the Korea Institute of Building Construction
• /
• v.17 no.6
• /
• pp.545-557
• /
• 2017

The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.

• Proceedings of the Korean Society of Disaster Information Conference
• /
• 2022.10a
• /
• pp.347-348
• /
• 2022

본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 객관성 있게 수행하기 위하여 무인이동체를 통해 촬영된 이미지를 딥러닝 기반 분석기술을 활용하여 손상 자동으로 분석 하기위한 기술을 연구하였다. 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 추출하여 딥러닝 분석 모델을 생성하고 학습한 분석 모델을 적용한 시스템을 실제 현장에 적용 테스트를 수행하였으며 학습 구현된 분석모델의 검측 재현율을 검토한 결과 평균 95%이상의 감지성능을 검토할 수 있었다. 개발 제안된 자동손상분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하며 철도 유지관리 분야에서 무인이동체를 활용한 외관조사 업무를 수행함에 있어 기존 대비 객관적인 결과도출과 소요시간, 비용저감이 가능할 것으로 기대된다.

• Journal of Applied Reliability
• /
• v.12 no.3
• /
• pp.201-213
• /
• 2012

A gear type lubrication pump is an essential component of the powertrain and has a major role for supplying oil to the gears and bearings in automatic transmission with a hydraulic clutch. Therefore, the durability test code design of lubrication pump is very important to ensure the reliability of the entire transmission and the vehicle. In this study, the design process for the life testing of lubrication pump has been generalized by four steps. The four design steps of the life testing of lubrication pump consist of the configuration of load spectrum, rain flow counting and analysis of load level, the equivalent damage assessment and test code generation. In fact, the load spectrum should be obtained from the field operating condition but that kind of data is the top secret of manufacturers. This is not open to the public in general. So we could use the artificially simulated load spectrum instead of field obtained one and focused on the development of the general process for designing the life test of a gear type lubrication pump. Reliability goals for lubrication pump, pressure, torque, temperature and load spectrum, if present, as appropriate for the given test conditions, accelerated life testing for the lubrication pump can be designed by the developed design steps.

• Smart Structures and Systems
• /
• v.30 no.5
• /
• pp.501-511
• /
• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.10
• /
• pp.2113-2120
• /
• 2012

The study of automatic monitoring and analysis of marine environment in Korea is not enough. Recently, the marine monitoring technology is actively being studied since the sea is a rich repository of natural resources that is taken notice in the world. In particular, the marine environment data should be collected continuously in order to understand and analyze the marine environment, however the marine environment monitoring is limited in many area yet. The prediction of marine disaster by automatic collecting marine environment data and analyzing the collected data can contribute to minimized the damages with respect to marine pollution of oil spill and fisheries damage by red tide blooms and marine environment upsets. In this paper, we proposed the marine environment monitoring and analysis system model. The proposed system automatically collects the marine environment information for monitoring the marine environment intelligently. Also it predicts the marine disaster by analyzing the collected ocean data.

• Korean Journal of Computational Design and Engineering
• /
• v.12 no.4
• /
• pp.308-320
• /
• 2007

A multi-agent and web based engineering framework concerning the automation of fatigue durability analysis for welded bogie frame of railway vehicles is presented. Mostly, this kind of design or analysis includes complex workflow, huge amounts of information processing, and problem solving. Macro programs of I-DEAS, APDL of ANSYS, and in-house fatigue code are utilized for parametric geometry representation, automatic mesh generation, static stress analysis, fatigue durability analysis, post-processing, and data sorting. The engineering framework is implemented on the JADE. Since every task requires a fairly complex process and specialized knowledge, the multi-agent based framework is very useful to keep the independency among several disciplines or tasks and to use distributed hardware and software resources. All engineering programs are integrated by XML wrapper. Related database of the engineering framework and web based user interfaces are also developed. A parametric study is carried out to take into account the effect of geometrical change of transom support bracket on its cumulative fatigue damage. The developed engineering framework reduced remarkably the time and costs required in designing and solving engineering problems.

• Journal of the Korea Convergence Society
• /
• v.10 no.6
• /
• pp.33-39
• /
• 2019

As cyber attack methods are becoming more intelligent, incidents such as security breaches and international crimes are increasing. In order to predict and respond to these cyber attacks, the characteristics, methods, and types of attack techniques should be identified. To this end, many security companies are publishing security intelligence reports to quickly identify various attack patterns and prevent further damage. However, the reports that each company distributes are not structured, yet, the number of published intelligence reports are ever-increasing. In this paper, we propose a method to extract structured data from unstructured security intelligence reports. We also propose an automatic intelligence report analysis system that divides a large volume of reports into sub-groups based on their topics, making the report analysis process more effective and efficient.