• Korean Journal of Soil Science and Fertilizer
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• v.4 no.1
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• pp.13-19
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• 1971

In order to establish the method of improving ill drained paddy soil where the accumulation of absorption inhibitor is worried in the earlier stages of rice growth, proper soil is selected and an field experiment is designed having treatments such as lime materials, none sulfate fertilizers, boron and straw etc. The data of yield and plant analysis in different stages of rice growth is eveluated and discussed to obtain following summaries. (1) Significant yield increase was made by the treatment of lime materials such as slacked lime or wollastonite powder, materials inhibiting the activity of microorganisms such as boron and of none sulfate fertilizers lacking inhibitor producing sources. (2) The crop scientifice causes of decreasing yield are the decreasing the number of panicles per hill, grains per panicle and the weight of grains. (3) The plant nutritional causes of decreasing yield are the lowering of nitrogen content throughout the life, phosphate content since young premodia formation stage of plant and the decreased content of magnesium, calcium and silicate in straw at harvesting stage. (4) The causes of lowering the content of various elements in rice plant grown in ill drained paddy soil are suggested as root damage by producing and accumulating absorption inhibitors such as organic acids and hydrogen sulfide etc, from the following observed facts; (a) In young premodia formation stage, attaining to the maximum production and accumulation of absorption inhibitor, the phosphate accumulation in plant was smaller in the phosphate plots than without phosphate plots and much higher in the neutralized plots by adding lime materials. (b) In the plots of straw addition, the potassium content in plant at the young premodia formation stage is very low probabley due to root damage by absorption inhibitor produced from the process of straw decomposition but higher at the stage of harvesting probably due to the immetabolic negative absorption of damaged roots. (c) The effect of boron, known as the inhibitor of microorganism activity to decompose organic matter, is apparent. (d) The effect of nonsulfate fertilizer treatment, having no source of producing inhibitor such as hydrogen sulfide, was significant. (e) All the yield components, decided around the young premodia formation stage attaining to the maximum inhibitor concentration in soil and minimum root activity, are significantly decreased.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.813-823
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• 2020

Purpose: In order to maximize the stability and productivity of the work through simulation prior to high-risk facilities and high-cost work such as dismantling the facilities inside the reactor, we intend to use digital twin technology that can be closely controlled by simulating the specifications of the actual control equipment. Motion control errors, which can be caused by the time gap between precision control equipment and simulation in applying digital twin technology, can cause hazards such as collisions between hazardous facilities and control equipment. In order to eliminate and control these situations, prior research is needed. Method: Unity 3D is currently the most popular engine used to develop simulations. However, there are control errors that can be caused by time correction within Unity 3D engines. The error is expected in many environments and may vary depending on the development environment, such as system specifications. To demonstrate this, we develop crash simulations using Unity 3D engines, which conduct collision experiments under various conditions, organize and analyze the resulting results, and derive tolerances for precision control equipment based on them. Result: In experiments with collision experiment simulation, the time correction in 1/1000 seconds of an engine internal function call results in a unit-hour distance error in the movement control of the collision objects and the distance error is proportional to the velocity of the collision. Conclusion: Remote decomposition simulators using digital twin technology are considered to require limitations of the speed of movement according to the required precision of the precision control devices in the hardware and software environment and manual control. In addition, the size of modeling data such as system development environment, hardware specifications and simulations imitated control equipment and facilities must also be taken into account, available and acceptable errors of operational control equipment and the speed required of work.

• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.66-77
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• 2023

Background: Along with the increase in consumer interest in and consumption of tattoo products, the controversy over harmful heavy metals associated with the use of tattoo cosmetics is also increasing. Therefore, investigation of hazardous metals in these tattoo products is needed. Objectives: This study was performed to provide useful data for establishing reasonable standards to securely manage tattoo cosmetics, tattoo stickers, and tattoo inks distributed in the market. Methods: Thirteen kinds of hazardous metal contents (Pb, As, Cd, Sb, Ni, Co, Cu, Cr, Se, Ba, Zn, Sn, and Hg) were analyzed for 23 tattoo cosmetics, ten tattoo stickers, and 16 tattoo inks. Hg was measured through the combustion-gold amalgamation method, and other hazardous metals were measured by inductively coupled plasma-mass spectrometry (ICP-MS) after acidic decomposition using a microwave apparatus. Results: The detected ranges of Pb, As, Cd, Sb, Ni, and Hg in tattoo cosmetics were 0.07~1.18, 0.06~0.41, ND~0.07, 0.01~3.44, 0.12~2.75, and ND~0.01 ㎍/g, respectively. All of the hazardous metals detected were below the recommended maximum standards of the Ministry of Food and Drug Safety. The mean amount of Pb detected in tattoo stickers for children was 0.24 ㎍/kg and Cd was not detected, meaning both metals met the recommended criteria. There was no statistically significant difference in all measured metals between children's tattoo stickers and adults' tattoo stickers. In the results of the study on the hazardous metal content of tattoo inks, four products (25%) for Pb, one product (6%) for As, 13 products (81%) for Ni, four products (25%) for Cu, and five products (31%) for Zn exceeded the recommended standards approved by the government. The highest predicted exposure amount for hazardous metals exceeding the recommended level of tattoo inks in a single tattooing was 5.69 ㎍/kg for Ni, 8.51 ㎍/kg for Zn, 0.44 ㎍/kg for Pb, 8.07 ㎍/kg for Cu, 0.44 ㎍/kg for As, and 71.36 ㎍/kg for Ba. Conclusions: It is necessary to prepare criteria for content limitation for the management of Co, Cr, Ba and Se tattoo cosmetics, and tattoo inks require thorough quality control.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.103-114
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• 2023

The Christian Museum of Gongju Jeil Church was first built in 1931 and was largely damaged during the Korean War, but the walls and chimneys have been preserved. This building has a high architectural values in that the chapel was reconstructed in 1956, and maintains its original form through repair of damaged parts rather than new construction. The stained glass windows were as installed in 1979 and has a great significance in the Dalle de Verre method using lump glass. However, some of the stained glass damaged partially, such as various cracks and splits, and vertical and horizontal cracks in the joint fillers of supporting the colored glass. As the structural materials of the stained glass window, an iron frame and cement mortar filled with it were used, and corrosion of iron, cracking of mortar and granular decomposition appear partially due to weathering. In the joint fillers, the content of Ca and S is very high, indicating that gypsum were used as admixtures, and the gypsums grow in a rhombohedral and forms a bundle, which is investigated to have undergone recrystallization. As a result of modeling the ultrasonic velocity at the joint fillers, the left and right windows at the entrance show relatively weak in the range of 800 to 1,600m/s, and the lower right corner of the altar window and the upper left corner of the center window were also 1,000 to 1,800m/s, showing relatively low physical properties. And gypsums produced during the neutralization of lime mortar were detected in the joint fillers and contaminants on the surface. Such salts may cause damage to the joint material due to freezing and thawing, so appropriate preventive conservation is required. Also, since various damage types are complexly appearing in stained glass window and joint filler, customized conservation treatment should be reviewed through clinical tests.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.1
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• pp.13-21
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• 2022

Mushroom waste medium refers to the waste biomass generated after mushroom cultivating. And, the burden of treatment on mushroom farmhouse is increasing due to the absence of appropriate treatment method and increase of treatment costs of the mushroom waste medium. In this study, in order to assess the energy value of mushroom waste medium by an anaerobic digestion, methane potential and anaerobic organic matter decomposition characteristics were investigated. The theoretical methane potential(B_th) of mushroom medium(MM) was 0.481 Nm³-CH₄/kg-VS_added, and the B_th of mushroom waste medium(MWM) was 0.451 Nm³-CH₄/kg-VS_added. The biochemical methane potential(B_u-exp) of MWM was increased by 18% from 0.155 for MM to 0.183 Nm³-CH₄/kg-VS_added for MWM. In the reaction kinetics analysis by the Modified Gompertz model, the maximum methane production rate(R_m) was increased from 4.59 for MM to 7.21 mL/day for MWM and the lag growth phase time(λ) was decreased from 2.78 for MM to 1.96 days for MWM. In the reaction kinetics analysis by the parallel first order kinetics model, the easily degradable organic matter(VS_e) content was increased by 5.89% and the persistently degradable organic matter(VS_p) content was 2.03% in MWM, and the non-degradable organic matter(VS_NB) content was decreased by 7.85%. Therefore, it was evaluated that the anaerobic digestion efficiency of MWM was increased. The anaerobic digestion efficiency of MWM was assessed to be more improved than that of MM.

• Clean Technology
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• v.30 no.1
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• pp.37-46
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• 2024

Environmental problems caused by plastic waste have been continuously growing around the world, and plastic waste is increasing even faster after COVID-19. In particular, PP and PE account for more than half of all plastic production, and the amount of waste from these two materials is at a serious level. As a result, researchers are searching for an alternative method to plastic recycling, and plastic pyrolysis is one such alternative. In this paper, a numerical study was conducted on the pyrolysis behavior of non-condensable gas to predict the chemical reaction behavior of the pyrolysis gas. Based on gas products estimated from preceding literature, the behavior of non-condensable gas was analyzed according to temperature and residence time. Numerical analysis showed that as the temperature and residence time increased, the production of H₂ and heavy hydrocarbons increased through the conversion of the non-condensable gas, and at the same time, the CH₄ and C₆H₆ species decreased by participating in the reaction. In addition, analysis of the production rate showed that the decomposition reaction of C₂H₄ was the dominant reaction for H₂ generation. Also, it was found that more H₂ was produced by PE with higher C₂H₄ contents. As a future work, an experiment is needed to confirm how to increase the conversion rate of H₂ and carbon in plastics through the various operating conditions derived from this study's numerical analysis results.

• Applied Biological Chemistry
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• v.13 no.2
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• pp.111-129
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• 1970

As a series of studies on the nucleic acids and their related substances 210 samples were collected from 76 places such as farm soil, compost of heap, nuruk and meju to obtain microbial strains which produce 5'-phosphodiesterase. From these samples total of 758 strains were isolated by the use of dilution pour plate method. For all isolated strains primary screening of the productivity of RNA depolymerase was performed and useful strains with regard to 5'-phosphodiesterase productivities were identified. For these useful strains optimum condition, the effect of various compounds on the activity of 5'-phosphodiesterase, and the optimum condition for enzyme reaction were discussed. The quantitative of 5'-mononucleotides produced by the action of 5'-phosphodiesterase was performed using anion-exchange column chromatography and their identified was done by paper chromatography, thinlayer chromatography, ultra violet spectrophotometry, and characteristic color reaction using carbazole and schiff's reagent. (1) Penicillium citreo-viride PO 2-11 and Streptomyces aureus SOA 4-21 from soil were identified as a potent 5'-phosphodiesterase producing strains. (2) Optimum culture conditions for Penicillium citreo-viride PO 2-11 strain isolated were found to be pH 5.0 and $30^{\circ}C$, and the optimum conditions for enzyme action of 5'-phosphodiesterase were pH 4.2 and $60^{\circ}C$. Best carbon source for the production of 5'-phosphodiesterase was found to be sucrose and ammonium nitrate for nitrogen source. Addition of 0.01% corn steep liquor or yeast extract exhibited 20% increase in the amount of 5'-phosphodiesterase production compared to the control. 5'-phosphodiesterase produced by this strain was activated by $Mg^{++},\;Ca^{++},\;Zn^{++},\;Mn^{++}$ and was inhibited by EDTA, citrate, $Cu^{++},\;CO^{++}$. 5'-phosphodiesterase produced 5'-mononucleotide from RNA at a rate of 65.81%, and among the 5'-mononucleotides accumulated 5'-GMP only was found to have flavorous and the strain was also found lack of 5'-AMP deaminase. Productivity of flavorous 5'-GMP was found to be 186.7mg per gram of RNA. (3) Optimum culture canditions for the isolated Streptomyces aureus SOA 4-21 strain were pH 7.0 and $28^{\circ}C$, and the optimum conditions for the action of 5'-phosphodiesterase were pH 7.3 and $50^{\circ}C$. The best carbon source for 5'-phosphodiesterase production was found to be glucose and that of nitrogen was asparagine. Addition of 0.01% yeast extract exhibited increased productivity of 5'-phosphodiesterase by 40% compared to the non-added control. 5'-phosphodiesterase produced by this strain was activated by $Ca^{++},\;Zn^{++},\;Mn^{++}$ and was inhibited by citrate, EDTA, $Cu^{++}$. It was also found that the strain produce 5'-AMP deaminase in addition to 5'-phosphodiesterase. For this reason although decomposition rate was 63.58% the accumulation of 5'-AMP, 5'-CMP, 5'-GMP and 5'-UMP occurred by the breakdown of RNA. In the course of these reaction 5'-AMP deaminase converted 60% of 5'-AMP thus produced into 5'-IMP and flavorous 5'-mono nucleotide production was significantly increased by this strain over the above mentioned one. Production rates were found to be 171.8mg per grain of RNA for 5'-IMP and 148.2mg per gram of RNA for 5'-GMP, respectively.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Applied Biological Chemistry
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• v.7
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• pp.105-117
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• 1966

This experiment was conducted to investigate the effet of phthalimido-methyl-O,O-dimethyl-phosphorodithioate (Imidan) known as an acaricide and its possible metabolic products on the growth of plant, when sprayed on the leaves of rice plant. The results are summarized as follows. 1) Possible metabolic products of Imidan, the following compounds were synthesized or recrystallized for the present experiment a) N-Hydroxymethyl phthalimidem b) Phthalimide c) Phthalamidic acid d) Phthalic acid e) Anthranilic acid f) p-Amino benzoic acid g) p-Hydroxy benzoic acid h) Benzoic acid 2) Among the above materials, a), c), d), e), and Imidan were dissolved in a buffer solution respectively to be 10 and 20 p.p.m. and tested with the wheat coleoptile straight growth method. According to the results, Imidan inhibited the growth of coleoptile in both 10 and 20 p.p.m., whereas the others showed much better growth than the control, especially phthalamidic acid in 10 p.p.m. It appears that Imidan itself inhibits the coleoptile growth, whereas the metabolites derived from Imidan through various metabolisms, including hydrolysis in plant tissues show growth-regulating activity. (refer: Table 1, Fig. 1) 3) 20, 100 and 200 p.p.m. solutions of Imidall emulsion in xylene f·ere prepared. The lengths of shoot and root of rice seeds germinated on the re-respective media were measured after 12 days. The data showed that root was much more elongated in Imidan 20 p.p.m., whereas shoot in Imidan 100 p.p.m., respectively, than in the xylene control. An interesting finding was that xylene used as solvent had a tendency to inhibit seriously the root growth of rice seed. (refer: Table 2,5). 4) The emulsions of concentrations in 10, 25, 50 and 100 p.p.m's of control, Imidan, N-hydroxy methyl phthalimide, anthranilic acid, and phthalmide, respectively, were sprayed twice on the rice plant on pot. After a certain period of time lengths of rice culms were measured, showing that plots treated with Imidan and N-hydroxy methyl phthalimide exhibited much more growth than those of control and the others. 5) Loaves and stems of rice plant were sampled and extracted with dried acetone at the intervals of 3-, 5-, 7-, and 14 days after treated with Imidan 250 p.p.m. emulsion. This sample extracted with acetone was purified by means of prechromatographic purification method with acetonitrile and paperchromatographed to detect the following metabolic products. Imidan (Rf: 0.97-0,98), N-hydroxy-methyl phthalimide (Rf: 0.87) phthalimide (Rf: 0.86-0.87), phthalamidic acid (Rf: 0.13-0.14), phthalic acid (Rf: 0.02-0.03), benzoic acid (Rf: 0.42-0.43), p-amino benzoic acid or p-hydroxy benzoic acid (Rf: 0.08-0.09), and unidentified compounds (Rf: 0.73, 0.59, 0.33, 0.23. 0.07). In addition, in the early stages, such as 3- and 5 days nonhydrolyzed Imidan and its first hydrolytic product, N-hydroxymethyl phthalimide were detected in relatively large amounts, whereas in the last stages of 7- and 14 days due to further decomposition, the afore-mentioned two materials were reduced in the amount and p-hthalic, phthalamidic, benzoic, and p-Hydroxy benzoic, or p-Amino benzoic acids were detected in a considerably large amount. It is, therefore, believed that most of Imidan applied to the leaves of rice plant may be decomposed within almost 14 days. In the light of above observations it is considered that Imidan itself is not involved in plant growth regulating activity, whereas various phthaloyl derivatives produced in the course of metabolism (namelr, enzymic action) in plant tissues may have such effect.