• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.1
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• pp.42-51
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• 2011

Poisson model and Gamma-Poisson model are popularly used to analyze statistical behavior from defective data. The methods are based on binary criteria, that is, good or failure. However, manufacturing industries prefer polytomous criteria for classifying manufactured products due to flexibility of marketing. In this paper, I introduce two multivariate Gamma-Poisson(MGP) models and estimation methods of the parameters in the models, which are able to handle polytomous data. The models and estimators are verified on defective pixels of LCD manufacturing. Experimental results show that both the independent MGP model and the multinomial MGP model have excellent performance in terms of mean absolute deviation and the choice of method depends on the purpose of use.

• Journal of the Chungcheong Mathematical Society
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• v.33 no.2
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• pp.237-253
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• 2020

This article studies asymptotic stability of non-auto nomous linear systems with time-dependent coefficient matrices {A(t)}_t∈ℝ. The classical theorem of Levinson has been widely used to science and engineering non-autonomous systems, but systems with defective eigenvalues could not be covered because such a family does not allow continuous diagonalization. We study systems where the family allows to have upper triangulation and to have defective eigenvalues. In addition to the wider applicability, working with upper triangular matrices in place of Jordan form matrices offers more flexibility. We interpret our and earlier works including Levinson's theorem from the perspective of invariant manifold theory.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.167-174
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• 1995

The Dynamic programming method are developed for determining where to assign the inspection efforts in serial multi-stage production systems. The objective function is formulated to minimize the inspection and repairing costs. One of the major assumptions in this systems is that every assigned inspection stations should inspect the only items produced in manufacturing stages after the previous inspction station. The inspection stations can be assigned at every possible inspection stage after the manufacturing stages. Two type error is considered and screening inspection policy is assumed in this system and the defective items detected in tile inspection stations will be repaired or scraped by the defective types.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.4 no.5
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• pp.11-14
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• 1981

This study intends to decide the economic sample size based on the cost of sampling Inspection for destructive testing. The marginal percent defective is used as the lot tolerance percent defective (LTPD), and the Newton's iterative method is adopted to calculate the optimum sample size(n), given by the consumer's risk($\beta$ - risk) and the acceptance number(c).

• Journal of Korean Society for Quality Management
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• v.28 no.4
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• pp.67-74
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• 2000

A Bayesian burn-in procedure is developed for imited failure populations in which defective items fail soon after they are put in operation and non-defective ones never fail during he technical life of the items. Sequential schemes guaranteeing pre-specified outgoing quality of a product are derived based on prior information on the quality of a product and accumulated failure information up to the decision point. A numerical example is also provided.

• Interaction and multiscale mechanics
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• v.1 no.4
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• pp.397-409
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• 2008

The interfacial thermal resistance of pristine and defective carbon nanotubes (CNTs) embedded in low-density polyethylene matrix is studied in this paper. Interface thermal resistance in nanosystems is one of the most important factors that lead to the large variation in thermal conductivities in literature and the novelty of this paper lies in the estimation of the interfacial thermal resistance for defective nanotubes-systems. Thermal properties of CNT nanostructures are estimated using molecular dynamics (MD) simulations and the simulations were carried out for various temperatures by rescaling the velocities of carbon atoms in the nanotube. This paper also deals with the mesoscale thermal conductivities of composite systems, using effective medium theories by considering the size effect in the form of interfacial thermal resistance and also using the conventional micromechanical methods like Hashin-Shtrikman bounds and Wakashima-Tsukamoto estimates.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.12
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.476-481
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• 2009

The most troublesome artifacts in micro computed tomography (micro-CT) are ring artifacts. The ring artifacts are caused by non-uniform sensitivity and defective pixels of the x-ray detector. These ring artifacts seriously degrade the quality of CT images. In flat-panel detector based micro-CT systems, the ring artifacts are hardly removed by conventional correction methods of digital radiography, because very small difference of detector pixel signals may make severe ring artifacts. This paper presents a novel method to remove ring artifacts in flat-panel detector based micro-CT systems. First, the bad lines of a sinogram which are caused by defective pixels of the detector are identified, and then, they are corrected using a cubic spline interpolation technique. Finally, a ring artifacts free image is reconstructed from the corrected projections. We applied the method to various kinds of objects and found that the image qualities were much improved.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1009-1011
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• 2002

Some defective cells in the battery bank of power systems using batteries result in deterioration of the performance of the total battery bank. Consequently, the battery bank can't perfectly back up the system in occurrence of any power problems and the overcharge of defective cells may lead to their explosion or the occurrence of fire. The developed battery management system in this study enables operators to telemeter and analyze internal resistance, voltages, currents, and temperatures of batteries at remote sites through a PC, so they can detect defective cells before the occurrence of power problems. And adoption of this system ensures extension of battery life.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.211-216
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• 2011

The detection of defective parts in a factory is usually performed by the human eye. Therefore, heavy manpower is in demand for minor enterprises. An image processing system is desired to solve this drawback. However, due to the variety of the products characteristics, an general algorithm is needed that can adapt to these characteristics. Therefore, in this paper, the key pad parts' characteristics which need to be dealt with are analyzed in order to embody the image processing algorithm that is suggested. The experimental results show the probability of detecting a defective part is 95% with a detection time of 0.203 seconds, on the average.