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Kim, Seung-Hyun,Lee, Joonhee,Ahn, Seonghee,Song, Young-Sin,Kim, Dong-Kyum,Kim, Byungjoo Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.2
A mass balance method established in this laboratory was applied to determine the purity of an endosulfan-II pure substance. Gas chromatography-flame ionization detector (GC-FID) was used to measure organic impurities. Total of 10 structurally related organic impurities were detected by GC-FID in the material. Water content was determined to be 0.187% by Karl-Fischer (K-F) coulometry with an oven-drying method. Non-volatile residual impurities was not detected by Thermal gravimetric analysis (TGA) within the detection limit of 0.04% (0.7 ${\mu}g$ in absolute amount). Residual solvents within the substance were determined to be 0.007% in the Endosulfan-II pure substance by running GC-FID after dissolving it with two solvents. The purity of the endosulfan-II was finally assigned to be ($99.17{\pm}0.14$)%. Details of the mass balance method including interpretation and evaluating uncertainties of results from each individual methods and the finally assayed purity were also described.
Image Enhanced Machine Vision System for Smart Factory
Kim, ByungJoo The Institute of Internet 2021 International Journal of Internet, Broadcasting an Vol.13 No.2
Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.
Kim, Donghwi,Lee, Joonhee,Kim, Byungjoo,Kim, Sunghwan American Chemical Society 2018 ANALYTICAL CHEMISTRY - Vol.90 No.20
<P>In this study, paper-based ionization techniques-paper spray ionization (PSI) and paper spray chemical ionization (PSCI)-were evaluated and applied for high-resolution mass spectrometry (MS)-based analysis of natural organic matter (NOM). Methanol:isopropyl alcohol (50:50, v/v) and ethanol emerged as good spray solvents for PSI, and hexane:dichloromethane (50:50, v/v) was a good spray solvent for PSCI. PSI-MS spectra could be obtained with NOM samples on the microgram scale, which is a critical advantage over conventional electrospray ionization (ESI)-MS when the amount of available sample is limited. In addition, PSI is more tolerant to salt contamination than ESI for NOM analysis. PSCI preferentially ionized less polar compounds, which may not be ionized well using ESI. Therefore, PSCI can be used as a complementary method to ESI or PSI. Comparison of the numbers of peaks obtained with ESI-, PSI-, and PSCI-MS showed that employing PSI and PSCI can increase the number of compounds that can be detected by high-resolution MS. In conclusion, the data presented in this study showed that PSI and PSCI are suitable ionization techniques for NOM analysis. To the best of our knowledge, this is the first study evaluating and applying PSI and PSCI for NOM analysis.</P> [FIG OMISSION]</BR>
Kim, In Jung,Kim, Byungjoo,Hwang, Euijin Korean Chemical Society 2014 Bulletin of the Korean Chemical Society Vol.35 No.4
In our previous articles, an approach has been proposed for the evaluation of the uncertainty of overall result from multiple measurements. In the approach, uncertainty sources were classified into two groups: the first including those giving same 'systematic' effect on each individual measurement and the second including the others giving 'random' effect on each individual measurement and causing a variation among individual measurement results. The arithmetic mean of the replicated measurements is usually assigned as the value for the overall result. Uncertainty of the overall result is determined by separately evaluating and combining an overall uncertainty from sources of the 'systematic' effect and another overall uncertainty from sources of the 'random' effect. This conceptual approach has been widely adopted in chemical metrology society. In this study, further logical proof with more detailed mathematical expressions is provided on the approach.
Kim, Donghwi,Yim, Un Hyuk,Kim, Byungjoo,Cha, Sangwon,Kim, Sunghwan American Chemical Society 2017 ANALYTICAL CHEMISTRY - Vol.89 No.17
<P>Sensitivity is an important factor determining successful mass spectrometry (MS) analysis of metabolome, protein, drugs, and environmental samples. Currently, nano-electrospray ionization (ESI) is widely used as a sensitive ionization method. However, application of nano-ESI is limited to polar molecules and there is no atmospheric pressure ionization technique developed that can be used for MS analysis of low- and nonpolar compounds with sensitivity that can match with nano-ESI. Herein, we propose paper spray chemical ionization (PSCI) as an ionization technique that can be used to analyze low- and nonpolar aromatic compounds with high sensitivity. PSCI is based on paper spray ionization utilizing corona discharge phenomenon. PSCI can sensitively and quantitatively detect down to picogram (or femtomole) levels of low and nonpolar aromatic compounds.</P>
A Study on Diabetes Management System Based on Logistic Regression and Random Forest
ByungJoo Kim The Institute of Internet 2024 International journal of advanced smart convergenc Vol.13 No.2
In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.