• Analytical Science and Technology
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• v.12 no.4
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• pp.290-298
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• 1999

A study on the application of impedance phase angle for redox titration, acid-base titration, chelate titration and precipitation titration has been carried out. A constant alternating current was passed between two platinum electrodes. One of them was a polarizable micro-electrode of $0.1cm^2$ or $0.026cm^2$ surface area and the other a non-polarizable large electrode of $1cm^2$ surface area dipped in the solution to be titrated. The impedance and the phase angle of the titration cell were measured with lock-in amplifier to obtain well behaved titration curve respectively. In titration of oxalic acid vs. potassium permanganate, the end-point was obtained successfully from the phase angle titration curve. In this experiment, the concentration of 0.0005 M to 0.05 M, the current of $50{\mu}A$ and the frequency of near 50 Hz were used. In titration of phosphoric acid vs. sodium hydroxide, the first end-point was obtained successfully on the optimum experimental condition of 0.001 M concentration, $50{\mu}A$ current and 25~97 Hz frequency. However, the end-point in titration of cupric sulfate vs. disodium-EDTA couldn't be obtained clearly. The end-point was obtained with the out-of-phase impedance curve on the experimental condition of 0.01 M concentration, $100{\mu}A$ current, 5~35 Hz frequency range. In titration of sodium chloride vs. silver nitrate, the end-point was obtained successfully on the experimental condition of 0.1 M concentration, $100{\mu}A$ current and 5~47 Hz frequency range. This study showed that the impedance phase angle was applicable for the detection of the end-points in redox titration curve, acid-base titration curve, chelate titration curve and precipitation titration curve.

• Corrosion Science and Technology
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• v.6 no.5
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• pp.227-232
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• 2007

The corrosion behavior of carbon steel (N80) in carbon dioxide saturated 1%NaCl solution with and without acetic acid or acetate was investigated by weight-loss test, electrochemical methods (polarization curve, Electrochemical impedance spectroscopy). The major objective is to make clear that the effect of acetic acid and acetate on the corrosion of carbon steel in $Co_2$ environments. The results indicate that either acetic acid or acetate accelerates cathodic reducing reaction, facilitates dissolution of corrosion products on carbon steel, and so promotes the corrosion rate of carbon steel in carbon dioxide saturated NaCl solution. All Nyquist Plots are consisting of a capacitive loop in high frequency region, an inductive loop in medial frequency region and a capacitive arc in low frequency region. The high frequency capacitive loop, medial frequency inductive loop and low frequency capacitive arc are corresponding to the electron transfer reaction, the formation/adsorption of intermediates and dissolution of corrosion products respectively. All arc of the measured impedance reduced with the increase of the concentration of Ac-, especially HAc. However, the same phenomenon is not notable after reducing pH value by adding HCl. HAc is a stronger proton donor and can be reduced directly by electrochemical reaction firstly. Ac- can't participate in electrochemistry reaction directly, but $Ac^-$ an hydrate easily to create HAc in carbon dioxide saturated environments. HAc is as catalyst in $Co_2$ corrosion. As a result, the corrosion rate was accelerated in the presence of acetate ion even pH value of solution increased.

• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.134-143
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• 1987

In order to observe the strawberry as the raw materials and to compare the color of strawberry's products, Hunter L,a,b tristimulate color values were measured physically by color difference meter. Food textural properties of strawberry were measured by Rheo textural meter for rheological properties of strawberry jam. According to results obtained, it showed that Hunter L,a,b tristimulus color values were affected by ripening time of strawberry and Hunter color values changed regularly on different pH. Deformation of red color pigment Hunter color values changed linearlly on different pH, therefore red color pigment of elderberry showed to be used as a food color agent. The first peak of strawberry in TPA curve was high as cherry, grape and pineapple Strawberry jam showed pseudoplastic characteristic and time dependence.

• Korean Journal of Environment and Ecology
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• v.37 no.5
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• pp.347-366
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• 2023

In the present study, the vegetation was classified using the phytosociological method and canonical-correlation analysis (CCA) was implemented to analyze correlation between community structure and environmental factors in the natural habitats of forest byproducts, especially medicinal plants, such as Cudrania tricuspidata, Sorbus commixta, and Hovenia dulcis, in 2021-2022 to provide primary ecological data to establish environmental conditions for wild vegetable cultivation. A total of 11 plots in five regions, 8 plots in three regions, and 17 plots in 5 regions were selected for the natural habitats of C. tricuspidata in southern Korea, S. commixta in high mountains, and H. dulcis in valleys of central Korea, respectively. The importance value in each community was respectively analyzed as follows, in C. tricuspidata community, the importance value of C. tricuspidata (61.10) was the highest, followed by Celtis sinensis, Pinus thunbergii, Neolitsea aciculata, Styrax japonica, Carpinus coreana, Quercus serrata, and Q. acutissima. In Sorbus commixta community, Q. mongolica (57.21) was the highest, followed by, S. commixta (42.58), Betula ermani, Tilia amurensis, A. pseudosieboldianum, A. tschonoskii var. rubripes, Cornus controversa, Magnolia sieboldii, and Taxus cuspidata. In H. dulcis community, H. dulcis (64.58) was the highest, followed by Zelkova serrata, Cornus controversa, A. mono, Q. serrata, C. cordata, and Juglans mandshurica. As the result of the analysis on DBH of the major species having the high importance value, in C. tricuspidata community, C. tricuspidata, C. sinensis, Neolitsea aciculata, and C. coreana show the density of normal distribution, so the dominant status of these species is likely to continue. In S. commixta community, S. commixta show the density of reverse J-shaped curve, so the dominant status of these species is likely to be stable, and Q. mongolica, B. ermani and T. amurensis, show the density of normal distribution, so the dominant status of these species is likely to continue. In H. dulcis community, C. cordata, and J. mandshurica show the density of reverse J-shaped curve, so the dominant status of these species is likely to be stable, and H. dulcis, Z. serrata, C. controversa and A. mono had a formality distribution, suggesting a continuous domination of these species over the other species for the time being. The results of CCA ordination analysis using 11 environmental factors and 30 communities of three taxa classified by TWINSPAN analysis revealed that the altitude showed the strongest correlation with the vegetation. C. tricuspidata community was distributed on the moderate and gentle northeastern slope at low altitude with the highest pH, C.E.C, Ca²⁺, and Mg² and various P₂O₅, whereas S. commixta community was distributed on the steep slope at high altitude with the highest O.M and T-N and lower P₂O₅, Ca²⁺, Mg²⁺, C.E.C and pH, which is the opposite tendency of the environment of C. tricuspidata community. H. dulcis community was distributed on the gentle northern slope at lower altitude with an average pH, O.M, T-N, Ca²⁺, Mg²⁺, and C.E.C, except higher P₂O₅.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.