• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.66-71
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• 1998

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1339-1347
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• 1997

The automation of final inspection and tuning process in the manufacturing of electric products is hot issue now, because it is the only part that has not been wholey automized yet, mainly due to the difficulties to handle so small size of VR which is the final tuning point in the most of electric products. For the automation of this process, at first the accurate measurement of position and orientation of SMD VR on PCB in real time is strongly needed. In this paper, a new image processing algorithm to detect the center position and orientation of target VR by using machine vision is proposed for automatic final tuning of the 8mm camcoder's performance. In the method, the outline feature of object is used actively. The usefulness of the proposed methods were tested by several experiments, and the results showed enough accuracy for both of position and orientation. Additatively, we discussed about the total visual system construction and preprocessing of image.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.158-163
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• 2002

This paper presents a multiple sensor system for rapid and high-precision coordinate data acquisition in the OMM (On-machine measurement) process. In this research, three sensors (touch probe, laser, and vision sensor) are integrated to obtain more accurate measuring results. The touch-type probe has high accuracy, but is time-consuming. Vision sensor can acquire many point data rapidly over a spatial range but its accuracy is less than other sensors. Also, it is not possible to acquire data for invisible areas. Laser sensor has medium accuracy and measuring speed among the sensors, and can acquire data for sharp or rounded edge and the features with very small holes and/or grooves. However, it has range- constraints to use because of its system structure. In this research, a new optimum sensor integration method for OMM is proposed by integrating the multiple-sensor to accomplish mote effective inspection planning. To verify the effectiveness of the proposed method, simulation and experimental works are performed, and the results are analyzed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.63-68
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• 2019

The dimensional quality of flexible foams is often difficult to be evaluated through general machine vision inspection methods due to the free deformation of the outer shape. For the evaluation of the dimensions of flexible foams, methods of estimating the size of the product through the expansion rate of the product surface are evaluated. Specimens with various dimensions and surface gratings are prepared, and the degree of surface expansion is measured through machine vision. The correlation, between the measured surface grid size and the actual size of test specimens, is analyzed. We further analyze the correlation between the size of test specimens and the position of the surface grid. This study provides a basis for estimating the actual dimensions of specimens by measuring the surface expansion of flexible foams.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.409-415
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• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.311-325
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• 2023

In the construction machinery rental market, there are frequent cases of sublease through large-scale rental companies or rental through mediation organizations without legal grounds. In addition, institutional improvement of the construction machine safety management system has been required due to concerns over the internalization of legal inspections due to the lack of type approval data and construction machine history management during the construction machine inspection process. The government is responsible for securing safety of construction machinery and promoting mechanization of construction machinery by efficiently managing the construction machinery market by setting safety management such as type approval, registration, and inspection of construction machinery. In order to efficiently implement this, it is required to establish a platform for renting construction machinery and collecting safety management information. We presented a plan to build a public platform for construction machinery to secure the soundness of the construction machinery rental market and to improve safety management.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.897-908
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• 2024

Evaluating the adequacy of facility safety inspection and diagnosis services performed by private enterprises is a time-consuming and administratively complex process. This study aims to analyze the determinants that could influence the rating of these safety inspection and diagnosis services using data analytics approach. Through a comparative analysis of several machine learning algorithms suitable for multi-class classification, we selected the model with the best performance (Random Forest) and identified the main determinants using the permutation importance technique. Among the variables examined, "contract value," "days of service performed" and "adherence to fair market value" were found to be strongly correlated with the rating assessments. Furthermore, we discovered that the skills and expertise of service performing personnel significantly impacted the rating. The results of this study can contribute to the enhancement of the current post-evaluation administrative processes and offer valuable insights into rating assessments by incorporating previously unexplored variables pertaining to both service providers and the services itself.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.24-29
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• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3056-3059
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• 2000

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.26-31
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• 2022

Changwon City has the second highest accident rate with 79.6 according to the city bus accident rate. In fact, 250,000 people use the city bus a day in Changwon, The number of accidents is increasing gradually. In addition, a recent fire accident occurred in the engine room of a city bus (CNG) in Changwon, which has gradually expanded the public's anxiety. In the case of business vehicles, the government conducts inspections with a short inspection cycle for the purpose of periodic safety inspections, etc., but it is not in the monitoring stage. In the case of city buses, the operation records are monitored using Digital Tacho Graph (DTG). As such, driving records, methods, etc. are continuously monitored, but inspections are conducted every six months to ascertain the safety and performance of automobiles. It is difficult to identify real-time information on automobile safety. Therefore, in this study, individual automobile management solutions are presented through machine learning techniques of inspection results based on driving records or habits by linking DTG data and Vehicle Inspection Management System (VIMS) data for city buses in Changwon from 2019 to 2020.