• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.5
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• pp.421-427
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• 2008

A robot crane truck is developed by the Bridge Inspection Robot Development Interface(BRIDI) for an automated and/or teleoperated bridge inspection. This crane truck looks similar to the conventional bucket crane, but is much smaller in size and light-weight. At the end of the telescoping boom which is 12m long, a robot platform is mounted which allows the operator to scan the bridge structure under the deck trough the camera. Boom vibration induced by wind and deck movement can cause serious problems in this scanning system. This paper presents a control system to mitigate such vibration of the robot boom. In the proposed control system, an actuator is installed at the end of the working boom. This control system is studied using a mathematical model analysis with LQ control algorithm and a scaled model test in the laboratory. The study indicates that the proposed system is efficient for the vibration control of the robot booms, thereby demonstrating its immediate applicability in the field.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.14-20
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• 2009

The goal of image processing is to improve the visual appearance of images for human viewers. The histogram is an important tool which can be used as basic data of digital image processing. Therefore, to effectively manage a histogram in digital image processing is very important. Currently machine vision systems are used in many appearance inspection fields instead of inspection by human. However, the appearance inspection result by machine vision system is mainly influenced by illumination of workplace. In this paper, we propose a histogram transform method for improving accuracy of machine visual inspection. The enhancement effect of area feature is obtained by performing proposed histogram transformation in area that needs improvement The proposed algorithm is verified by appearance inspection of jig plate samples.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.11-17
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• 2007

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. However the inspection result of machine vision is changed by the illumination of workplace. Therefore we have used a power-law transformation and histogram specification in this paper for improvement of vision inspection accuracy. As a result of these power-law transformation and histogram specification algorithm, we could increase the exactness of vision inspection and prevent system error from physical and spirit condition of human. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.84-90
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• 2004

Today, automobile world market is highly competitive. In order to strengthen the competitiveness, quality of automobile is recognized as important and efforts are being made to improve the quality of manufactured components. The directional ability of automobile has influence on driver directly and hence it must be solved on the preferential basis. In the present research, an automated vision system has been developed to inspect the front chassis module. To interpret the inspection data obtained for front chassis module, new interpreting algorithm have been developed. Previously the control of tolerance of front chassis module was done manually. With the help of the new algorithm developed, the dimension is calculated automatically to check whether the front chassis module is within the tolerance limit or not.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.6 s.249
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• pp.713-721
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• 2006

Micro-drill wear deteriorates accuracy and productivity of the micro components. In order to improve productivity and qualify of micro components, it is required to investigate micro-drill wear exactly. In this study, a machine vision system is proposed to measure the wear of micro-drills using a precision servo stage. Calibration experiments are conducted to compensate for the machine vision system. In this paper, worn volume, area and length are defined as wear amounts. Micro-drill wear is reconstructed as the 3D topography and the quantized wear amount by using the shape from focus (SFF) method and wear parameters. Experiments have been conducted with HSS twist micro-drills and SM45C carbon steel workpieces. Validity of the proposed machine vision system is confirmed through experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.453-458
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• 2016

One of the most common applications of machine vision is quality inspections in automated production. In this study, a welding inspection system that is controlled by a PC and a PLC equipped with a multi-camera setup was developed. The system was designed to measure the primary dimensions, such as the length and width of the welding areas. The TCP/IP protocols and multi-threading techniques were used for parallel control of the optical components and physical distribution. A coaxial light was used to maintain uniform lighting conditions and enhance the image quality of the weld areas. The core image processing system was established through a combination of various algorithms from the OpenCV library. The proposed vision inspection system was fully validated for an actual weld production line and was shown to satisfy the functional and performance requirements.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.138-140
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• 2004

In this study, we develop a remocon inspection automatic system using machine vision technique. By our proposed inspection system, the inspection accuracy and processing time was considerably improved.

• Korean Journal of Optics and Photonics
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• v.29 no.4
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• pp.149-158
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• 2018

A generalized structural design equation can be used to simplify and systematize a telecentric lens system composed of multiple lenses, as a creative design method of the authors. Through this structural equation, we have investigated the feasibility and design methodology of a telecentric lens equipped with a conventional smartphone camera for machine vision. As a result, we could verify and present a useful, generalized structural equation termed the $f{\theta}$ formula, being able to divide and combine the whole telecentric lens system into two modularized lens groups.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.29-34
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• 2024

Since the outbreak of COVID-19, there has been a surge in sports conducted through online platforms due to the increase in remote and non-contact activities. Billiards, being suitable for online platforms, has received much attention, leading to research on detecting the position and trajectory of balls. In this paper, we propose a new method utilizing machine vision to detect the position of the balls accurately. The proposed method detects the outline of the ball using the Canny edge detection and then employs simple correlation to determine its position. This correlation-based approach offers satisfactory system performance and is easily applicable in practical systems due to its low implementation complexity and robustness to noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.467-468
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• 2021

In order to improve the performance of the indoor positioning system using BLE beacons, a receiver that measures the angle of arrival among the direction finding technologies supported by BLE5.1 was manufactured and analyzed by machine learning to measure the optimal position. For the creation and testing of machine learning models, k-nearest neighbor classification and regression, logistic regression, support vector machines, decision tree artificial neural networks, and deep neural networks were used to learn and test. As a result, when the test set 4 produced in the study was used, the accuracy was up to 99%.