• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1163-1168
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• 2001

The ranging systems measure range data in three-dimensional coordinate from target surface. These non-contact remote ranging systems is widely used in various automation applications, including military equipment, construction field, navigation, inspection, assembly, and robot vision. The active ranging systems using time of flight technique or light pattern illumination technique are complex and expensive, the passive systems based on stereo or focusing principle are time-consuming. The proposed algorithm, that is based on cross correlation of projection profile of vertical edge, provides advantages of fast and simple operation in the range acquisition. The results of experiment show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2307-2310
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• 2005

A pressurizer is one of the major safety related equipment of nuclear power plants. In order to inspect and maintain the internal structures of a pressurizer, jumpers have to enter the pressurizer, in spite of the high dose exposure. Therefore, a tele-operated robotic manipulator has been developed, which consists of four parts with 2DOFs arms, a gripper, base frame, and control console. The task of this robotic manipulator is to extract the damaged electric heaters and inspect the internal structure of the pressurizer. The gripper hanging from the manipulator approaches the heaters and extracts the damaged heater by using a self-locking mechanism. In order to investigate the structural stability of the robotic manipulator, a stress analysis has been performed by using the ANSYS code. The results of this paper include the position control and vibration control of robotic gripper and the development of processing visual information for a vision sensor.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.19-34
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• 2015

One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1131-1138
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• 2006

This paper proposes a novel method for grading of underground communication conduits by laser projection image analysis. The equipment thrust into conduit consists of a laser diode, a light emitting diode and a camera, the laser diode is utilized for generating projection image onto pipe wall, the light emitting diode for lighting environment and the image of conduit is acquired by the camera. In order to segment profile region, we used a novel color difference model and multiple thresholds method. The shape of profile ring is represented as a minimum diameter and the Fourier descriptor, and then the pipe status is graded by the rule-based method. Both local and global features of the segmented ring shaped, the minimum diameter and the Fourier descriptor, are utilized, therefore injured and distorted pipes can be correctly graded. From the experimental results, the classification is measured with accuracy such that false alarms are less than 2% under the various conditions.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Explosives and Blasting
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• v.22 no.3
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• pp.57-64
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• 2004

There are two major types of underwater blasting at Korea, bridges and harbor construction work. Pier blasting for lay the foundation bridges construction is used dry excavation working (drilling and charging) after pump out water and then fire pump in water that is same as bench blasting. In contrast, underwater blasting for harbor construction and increase of harbor load depth is used to barge with digging equipment that is in oder to drilling on the surface and blasting work(charge, hook-up) under water. Thus, there are need to special concern such as charge method and hook-up method different from tunnel blasting work and bench blasting work. If do not use special concern breaks out dead pressure and mis fire because of there are so many difficult condition such as water pressure, obstruct field of vision. In this study underwater blasting at Busan Harbor Construction have consider with special concern that is plastic pipe charge method used to MegaMITE I and specialized buoy hook- up method make far initial system detonate on the surface used to TLD. The results is designed blast pattern charge per delay effect an inspection of verify between predict velocity and measure velocity. minimized break out mis fire consideration charge method, hook up method. According to result best underwater blasting design is 105mm drilling dia, MeGAMITE II, HiNLL Plus(non electric detonator).