• Journal of Korea Technology Innovation Society
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• v.16 no.2
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• pp.391-407
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• 2013

The paper tests the embodiment hypothesis by measuring the technological obsolescence of a specific year (1990)'s technology and investigating the relationship between labor productivity and technological obsolescence. This approach is based on Salter (1969) that emphasizes the economic aspect of technology. We use the rate of economic surplus as the proxy of technological obsolescence for 10 main industries, including food processing, textiles, chemicals, non-steel metals, steels, metal products, machinery, electronics, precision machinery, and transportation equipments. The result shows that the embodiment hypothesis is not accepted for the overall manufacturing sector. However, we found the vintage effect - a positive relationship between technological obsolescence and labor productivity over time - in textiles, chemicals, non-steel metals, metal products, electronics, and transportation equipments. Therefore, the government should support R&D investment as well as capital equipments investment for the industries with large vintage effect.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.56.2-56.2
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• 2016

We present a multi-aperture photometry pipeline for DEEP-South (Deep Ecliptic Patrol of the Southern Sky) time-series data, written in C. The pipeline is designed to do robust high-precision photometry and calibration of non-crowded fields with a varying point-spread function, allowing for the wholesale search and characterization of both temporal and spatial variabilities. Our time-series photometry method consists of three parts: (i) extracting all point sources with several pixel/blind parameters, (ii) determining the optimized aperture for each source where we consider whether the measured flux within the aperture is contaminated by unwanted artifacts, and (iii) correcting position-dependent variations in the PSF shape across the mosaic CCD. In order to provide faster access to the resultant catalogs, we also utilize an efficient indexing technique using compressed bitmap indices (FastBit). Lastly, we focus on the development and application of catalog-based searches that aid the identification of high-probable single events from the indexed database. This catalog-based approach is still useful to identify new point-sources or moving objects in non-crowded fields. The performance of the pipeline is being tested on various sets of time-series data available in several archives: DEEP-South asteroid survey and HAT-South/MMT exoplanet survey data sets.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.677-692
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• 2018

The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.

• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.549-567
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• 2015

A first-order moment method (FORM) reliability analysis is commonly used for structural stability analysis. It requires the values and partial derivatives of the performance to function with respect to the random variables for the design. These calculations can be cumbersome when the performance functions are implicit. A Gaussian process (GP)-based response surface is adopted in this study to approximate the limit state function. By using a trained GP model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis with a FORM, thereby reducing the number of stability analysis calculations. This dynamic renewed knowledge source can provide great assistance in improving the predictive capacity of GP during the iterative process, particularly from the view of machine learning. An iterative algorithm is therefore proposed to improve the precision of GP approximation around the design point by constantly adding new design points to the initial training set. Examples are provided to illustrate the GP-based response surface for both structural and non-structural reliability analyses. The results show that the proposed approach is applicable to structural reliability analyses that involve implicit performance functions and structural response evaluations that entail time-consuming finite element analyses.

• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.27-39
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• 2006

Many real world cases in material failure analysis do not follow perfectly the normal distribution. Forcing of the normality assumption may lead to inaccurate predictions and poor product quality. We examine the failure process of the internal bond (IB or tensile strength) of medium density fiberboard (MDF). We propose a forced censoring technique that closer fits the lower tails of strength distributions and better estimates extremely smaller percentiles, which may be valuable to continuous quality improvement initiatives. Further analyses are performed to build an accelerated common-shaped Weibull model for different product types using the $JMP^{(R)}$ Survival and Reliability platform. In this paper, a forced censoring technique is implemented for the first time as a software module, using $JMP^{(R)}$ Scripting Language (JSL) to expedite data processing, which is crucial for real-time manufacturing settings. Also, we use JSL to automate the task of fitting an accelerated Weibull model and testing model homogeneity in the shape parameter. Finally, a package script is written to readily provide field engineers customized reporting for model visualization, parameter estimation, and percentile forecasting. Our approach may be more accurate for product conformance evaluation, plus help reduce the cost of destructive testing and data management due to reduced frequency of testing. It may also be valuable for preventing field failure and improved product safety even when destructive testing is not reduced by yielding higher precision intervals at the same confidence level.

• Tribology and Lubricants
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• v.35 no.5
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• pp.274-285
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• 2019

Chemical mechanical polishing (CMP), which is a material removal process involving chemical surface reactions and mechanical abrasive action, is an essential manufacturing process for obtaining high-quality semiconductor surfaces with ultrahigh precision features. Recent rapid growth in the industries of digital devices and semiconductors has accelerated the demands for processing of various substrate and film materials. In addition, to solve many issues and challenges related to high integration such as micro-defects, non-uniformity, and post-process cleaning, it has become increasingly necessary to approach and understand the processing mechanisms for various substrate materials and abrasive particle behaviors from a tribological point of view. Based on these backgrounds, we review recent CMP R&D trends in this study. We examine experimental and analytical studies with a focus on substrate materials and abrasive particles. For the reduction of micro-scratch generation, understanding the correlation between friction and the generation mechanism by abrasive particle behaviors is critical. Furthermore, the contact stiffness at the wafer-particle (slurry)-pad interface should be carefully considered. Regarding substrate materials, recent research trends and technologies have been introduced that focus on sapphire (${\alpha}$-alumina, $Al_2O_3$), silicon carbide (SiC), and gallium nitride (GaN), which are used for organic light emitting devices. High-speed processing technology that does not generate surface defects should be developed for low-cost production of various substrates. For this purpose, effective methods for reducing and removing surface residues and deformed layers should be explored through tribological approaches. Finally, we present future challenges and issues related to the CMP process from a tribological perspective.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.393-402
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• 2015

Human resources are one of the most essential elements of an organization. In particular, the more closed a group is, the higher the value each member has. Previous studies have focused on personal attributes of individual, such as medical history, and have depended upon self-diagnosis to manage structures. However, this method has weak points, such as the timeconsuming process required, the potential for concealment, and non-disclosure of participants' mental states, as this method depends on self-diagnosis through extensive questionnaires or interviews, which is solved in an interactive way. It also suffers from another problem in that relations among people are difficult to express. In this paper, we propose a multi-faced diagnosis model based on social network analysis which overcomes former weaknesses. Our approach has the following steps : First, we reveal the states of those in a social network through 9 questions. Next, we diagnose the social network to find out specific individuals such as victims or leaders using the proposed algorithm. Experimental results demonstrated our model achieved 0.62 precision rate and identified specific people who are not revealed by the existing methods.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.5
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• pp.704-711
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• 2020

Objective: Muscle fiber types, numbers and area are crucial aspects associated with meat production and quality. However, there are few studies of pig muscle fibre traits in terms of the detection power, false discovery rate and confidence interval precision of whole-genome quantitative trait loci (QTL). We had previously performed genome scanning for muscle fibre traits using 183 microsatellites and detected 8 significant QTLs in a White Duroc×Erhualian F₂ population. The confidence intervals of these QTLs ranged between 11 and 127 centimorgan (cM), which contained hundreds of genes and hampered the identification of QTLs. A whole-genome sequence imputation of the population was used for fine mapping in this study. Methods: A whole-genome sequences association study was performed in the F₂ population. Genotyping was performed for 1,020 individuals (19 F₀, 68 F₁, and 933 F₂). The whole-genome variants were imputed and 21,624,800 single nucleotide polymorphisms (SNPs) were identified and examined for associations to 11 longissimus dorsi muscle fiber traits. Results: A total of 3,201 significant SNPs comprising 7 novel QTLs showing associations with the relative area of fiber type I (I_RA), the fiber number per square centimeter (FN) and the total fiber number (TFN). Moreover, one QTL on pig chromosome 14 was found to affect both FN and TFN. Furthermore, four plausible candidate genes associated with FN (kinase non-catalytic C-lobe domain containing [KNDC1]), TFN (KNDC1), and I_RA (solute carrier family 36 member 4, contactin associated protein like 5, and glutamate metabotropic receptor 8) were identified. Conclusion: An efficient and powerful imputation-based association approach was utilized to identify genes potentially associated with muscle fiber traits. These identified genes and SNPs could be explored to improve meat production and quality via marker-assisted selection in pigs.