• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2348-2356
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• 2021

As a relatively new radiation imaging method, the cosmic-ray muon scattering imaging technology can be used to prevent nuclear smuggling and is of considerable significance to nuclear safety. Proposed in this paper is a new reconstruction algorithm based on density clustering, aiming to improve inspection quality with better performance. Firstly, this new algorithm is introduced in detail. Then in order to eliminate the inequity of the density threshold caused by the heterogeneity of the muon flux in different positions, a new flux correction method is proposed. Finally, three groups of simulation experiments are carried out with the help of Geant4 toolkit to optimize the algorithm parameters, verify the correction method and test the inspection quality under shielded condition, and compare this algorithm with another common inspection algorithm under different conditions. The results show that this algorithm can effectively identify and locate nuclear material with low misjudging and missing rates even when there is shielding and momentum precision is low, and the threshold correcting method is universally effective for density clustering algorithms.

• 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 IEEK Conference
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• 2003.07c
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• pp.2533-2536
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• 2003

In this paper, we suggest the 3D Vision Inspection Algorithm which is based on the external shape feature, and is able to recognize the object. Because many objects made by human have the regular shape, if we posses the database of pattern and we recognize the object using the database of the object's pattern, we could inspect the objects of many fields. Thus, this paper suggest the 3D Vision inspection Algorithm using the Geometrical Pattern Matching by making the 3D database.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.637-640
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• 1999

In this Paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. The proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.6 respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.711-717
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• 2004

We propose a path planning method to improve the productivity of SMT (surface mount technology) inspection machines with an area camera. A unified method is newly proposed to determine the FOV clusters and camera sequence simultaneously. The proposed method is implemented by a hybrid genetic algorithm to increase the convergence speed. Comparative simulation results are then presented to verify the usefulness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.89-95
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• 2010

A new live-line insulator tester was developed to detect faulty insulators in 154kV power transmission lines. This paper is the second part of the two-part paper and deals with its inspection algorithm development. Unlike normal condition with low pollution and low humidity, the inspection data measured in the field under high pollution or high humidity showed that the voltage distribution of an insulator string has offsets in comparison with those of others and its insulation resistances are greatly decreased, which leads to wrong results of the existing inspection algorithms under such conditions. To solve this problem, we propose new diagnosis algorithms that can exactly detect faulty insulators from measured data regardless of environmental conditions. Its effectiveness was validated by live-line field tests in actual power lines.

• Textile Coloration and Finishing
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• v.21 no.1
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• pp.53-58
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• 2009

A real-time inspection system has been developed by combining CCD based image processing algorithm and a standard lighting equipment. The system was tested for defective fabrics showing nozzle contact scratch marks, which were one of the frequently occurring defects. Multi-resolution analysis(MRA) algorithm were used and evaluated according to both their processing time and detection rate. Standard value for defective inspection was the mean of the non-defect image feature. Similarity was decided via comparing standard value with sample image feature value. Totally, we achieved defective inspection accuracy above 95%.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.26-35
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• 1998

This paper presents a visual inspection algorithm looking for solder joint defects of IC chips on PCBs (Printed Circuit Boards). In this algorithm, seven features are proposed in order to categorize the solder joints into four classes such as normal, insufficient, excess, and no solder, and optimal back-propagation network is determined by error evaluation which depend on the number of neurons in hidden and out-put layers and selection of the features. In the end, a good accuracy of classification performance, an optimal determination of network structure and the effectiveness of chosen seven features are examined by experiment using proposed inspection algorithm.

• Journal of Platform Technology
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• v.10 no.3
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• pp.21-29
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• 2022

Recently, the scope of the smart factory has been expanded, and process research to minimize the part that requires manpower in many processes is increasing. In the case of detecting defects in the appearance of small products, precise verification using a vision system is required. Reliability and speed of inspection are inefficient for human inspection. In this paper, we propose an algorithm for inspecting product appearance defects using a machine vision system. In the case of the remote control targeted in this paper, the appearance is different for each product. Due to the characteristics of the remote control product, the data obtained using two cameras is compared with the master data after denoising and stitching steps are completed. When the algorithm presented in this paper is used, it is possible to detect defects in a shorter time and more accurately compared to the existing human inspection.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.145-146
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• 2008

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight cannot 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 proposed histogram specification in this paper for machine vision inspection accuracy. As a result of histogram specification algorithm, we could increase the exactness of visual inspection and prevent system error from physical and spirit condition of human. More specifically, average inspection error rate was 7.5[%] in existing inspection method but we could see 0.6[%] error rate after applying the algorithm which is presented in this paper.