• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.295-298
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• 2000

In this research, a novel sampling strategy for a CMM to inspect freeform surfaces is proposed. Unlike primitive surfaces, it is not easy to determine the number of sampling points and their locations for inspecting freeform surfaces. Since a CMM operates with slower speed in measurement than optical measuring devices, it is important to optimize the number and the locations of sampling points in the inspection process. When a complete inspection of a surface is required, it becomes more critical. Among various factors to cause shape errors of a final product, curvature characteristic is essential due to its effect such as stair-step errors in rapid prototyping and interpolation errors in NC tool paths generation. Shape errors are defined in terms of the average and standard deviation of differences between an original model and a produced part. Proposed algorithms determine the locations of sampling points by analyzing curvature distribution of a given surface. Based on the curvature distribution, a surface area is divided into several sub-areas. In each sub-area, sampling points are located as further as possible. The optimal number of sub-areas. In each sub-area, sampling points are located as further as possible. The optimal number os sub-areas is determined by estimating the average of curvatures. Finally, the proposed method is applied to several surfaces that have shape errors for verification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.434-437
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• 2003

Evaluation or the patterning accuracy of e-beam lithography machines requires a high precision inspection system that is capable of measuring the true xy-locations of fiducial marks generated by the e-beam machine under test. Fiducial marks are fabricated on a single photo mask over the entire working area in the form of equally spaced two-dimensional grids. In performing the evaluation, the principles of self-calibration enable to determine the deviations of fiducial marks from their nominal xy-locations precisely, not being affected by the motion errors of the inspection system itself. It is. however, the fact that only repeatable motion errors can be eliminated, while random motion errors encountered in probing the locations of fiducial marks are not removed. Even worse, a random error occurring from the measurement of a single mark propagates and affects in determining locations of other marks, which phenomenon in fact limits the ultimate calibration accuracy of e-beam machines. In this paper, we describe an uncertainty analysis that has been made to investigate how random errors affect the final result of self-calibration of e-beam machines when one uses an optical inspection system equipped with high-resolution microscope objectives and a precision xy-stages. The guide of uncertainty analysis recommended by the International Organization for Standardization is faithfully followed along with necessary sensitivity analysis. The uncertainty analysis reveals that among the dominant components of the patterning accuracy of e-beam lithography, the rotationally symmetrical component is most significantly affected by random errors, whose propagation becomes more severe in a cascading manner as the number of fiducial marks increases

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.755-764
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• 1996

A multispectral imaging system with selected wavelength optical filter was demonstrated feasible for food safety inspection. Intensified multispectral images of carcasses were obtained with visible/near-infrared optical filters(542-847 nm wavelengths) and analyzed. The analysis of textural features based on co-occurrence matrices was conducted to determine the feasibility of a multispectral image analyses for discriminating unwholesome poultry carcasses from wholesome carcasses. The mean angular second moment of the wholesome carcasses scanned at 542 nm wavelength was lower than that of septicemic (P$\leq$0.0005) and cadaver(P$\leq$0.0005) carcasses. On the other hand, for the carcasses scanned at 700nm wavelength , the feature values of septicemic and cadaver carcasses were significantly (P$\leq$0.0005) different from wholesome carcasses. The discriminant functions for classifying poultry carcasses into three classes (wholesome, septicemic , cadaver) were developed using linear and quadr tic covariance matrix analysis method. The accuracy of the quadratic discriminant models, expressed in rates of correct classification, were over 90% for the classification of wholesome, septicemic, and cadaver carcasses when textural features from the spectral images scanned at the wavelength of 542 and 700nm were utilized.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.125-132
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• 2008

This paper describes a mobile inspection robot with an automatic pipe tracking system for a feeder pipe inspection in a PHWR. The robot is composed of two inch worm mechanisms. One is for a longitudinal motion along a pipe, and the other is for a rotational motion in a circumferential direction to access all of the outer surfaces of a pipe. The proposed mechanism has a stable gripping capability and is easy to install. An automatic pipe tracking system is proposed based on machine vision techniques to make the mobile robot follow an exact outer circumference of a curved feeder pipe as closely as possible, which is one of the requirements of a thickness measurement system for a feeder pipe. The proposed sensing technique is analyzed to attain its feasibility and to develop a calibration method for an accurate measurement. A mobile robot and control system are developed, and the automatic pipe tracking system is tested in a mockup of a feeder pipe.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.59-65
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• 2016

As modern electronic devices get smaller and smaller, high-resolution, large Field-Of-View (FOV), fast, and cost-effective 3-dimensional (3-D) measurement is requested more and more. In particular, defect inspection machines using machine-vision technology nowadays require 3-D inspection as well as the conventional 2-D inspection. Phase Measuring Profilometry (PMP) is one of the fast non-contact 3-D shape measuring methods currently being extensively investigated in the electronic component manufacturing industry. The PMP system is well known and is successfully applied to measuring complex surface profiles with varying reflectance properties. However, for highly reflective surfaces, such as Ball Grid Arrays (BGAs), it has difficulty accurately measuring 3-D shapes. In this paper, we propose a new fast optical system that can eliminate the highly reflective saturated regions in BGA ball images. This is achieved by utilizing four Low Intensity Grating (LIG) images together with the conventional High Intensity Grating (HIG) images. Extensive experiments using BGA samples show a repeatability of under ${\pm}20um$ in standard deviation, which is suitable for most 3-D shape measurements of BGAs.

• Journal of Institute of Convergence Technology
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• v.6 no.1
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• pp.23-26
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• 2016

DC motor shafts have several defects such as double cut, deep scratch on surface, and defects in diameter and length. The deep scratches are due to collision among the other shafts. So the scratches are long and thin but their orientations are random. If the smallest enclosing box, i.e. oriented bounding box for a detective point group is found, then the size of the corresponding defect can be modeled as its diagonal length. This paper proposes an suface inspection algorithm for the DC motor shaft using the oriented bounding box. To evaluate the proposed algorithm, a test bed is made with a line scan CCD camera (4096 pixels/line) and two rollers mechanism to rotate the shaft. The experimental result on a pre-processed image with contrast streching algorithm, shows that the proposed algorithm sucessfully finds 150 surface defects and its computation time (0.291 msec) is enough fast for the requirement (4 seconds).

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.19-27
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• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.3
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• pp.62-66
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• 2024

A statistical approach to inspection of the 3-D shape of micro solder balls is proposed, where an optical method with spatially arranged LED and specular reflection is used. The reflected image captured by a vision system was analyzed to calculate the relative displacements of LED's in the image. Also, the statistics of displacements for the micro solder balls contained in a captured image are used to detect existing defects, and the usefulness of the proposed method is shown via experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.256-261
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• 2013

In the steel manufacturing area, researches for defect inspection receive a big attention for quality control. This paper proposes an algorithm to detect a scratch defect on steel billets. This algorithm takes ROIs (Regions of Interest), and extracts 11 features which represent properties of defect on a ROI. SVM (Support Vector Machine) is used to classify defect and normal ROIs. The algorithm classifies a frame image of a Billet as a defect image if there is one or more defect ROIs. In the experiments, the proposed algorithm had reliable classifying accuracy.