• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.1-8
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• 2019

This paper carried out a study to reduce damage from jellyfish whose population has increased due to global warming. The emergence of jellyfish on the beach could result in casualties from jellyfish stings and economic losses from closures. This paper confirmed from the preceding studies that the pattern of jellyfish's appearance is predictable through machine learning. This paper is an extension of The prediction model of emergence of Busan coastal jellyfish using SVM. In this paper, we used deep neural network to expand from the existing methods of predicting the existence of jellyfish to the classification by index. Due to the limitations of the small amount of data collected, the 84.57% prediction accuracy limit was sought to be resolved through data expansion using bootstraping. The expanded data showed about 7% higher performance than the original data, and about 6% better performance compared to the transfer learning. Finally, we used the test data to confirm the prediction performance of jellyfish appearance. As a result, although it has been confirmed that jellyfish emergence binary classification can be predicted with high accuracy, predictions through indexation have not produced meaningful results.

• Atmosphere
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• v.32 no.4
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• Journal of Gastric Cancer
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• v.21 no.1
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• pp.74-83
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• 2021

Purpose: No consensus exists on whether to preserve or ligate an aberrant left hepatic artery (ALHA), which is the most commonly encountered hepatic arterial variation during gastric surgery. Therefore, we aimed to evaluate the clinical effects of ALHA ligation by analyzing the perioperative outcomes. Materials and Methods: We retrospectively reviewed the data of 5,310 patients who underwent subtotal/total gastrectomy for gastric cancer. Patients in whom the ALHA was ligated (n=486) were categorized into 2 groups according to peak aspartate aminotransferase (AST) or alanine aminotransferase (ALT) levels: moderate-to-severe (MS) elevation (≥5 times the upper limit of normal [ULN]; MS group, n=42) and no-to-mild (NM) elevation (<5 times the ULN; NM group, n=444). The groups were matched 1:3 using propensity score-matching analysis to minimize confounding factors that can affect the perioperative outcomes. Results: The mean operation time (P=0.646) and blood loss amount (P=0.937) were similar between the 2 groups. The length of hospital stay was longer in the MS group (13.0 vs. 7.8 days, P=0.022). No postoperative mortality occurred. The incidence of grade ≥ IIIa postoperative complications (19.0% vs. 5.1%, P=0.001), especially pulmonary complications (11.9% vs. 2.5%, P=0.003), was significantly higher in the MS group. This group also showed a higher Comprehensive Complication Index (29.0 vs. 13.9, P<0.001). Conclusions: Among patients with a ligated ALHA, those with peak AST/ALT ≥5 times the ULN showed worse perioperative outcomes in terms of hospital stay and severity of complications. More precise perioperative decision-making tools are needed to better determine whether to preserve or ligate an ALHA.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1678-1686
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• 2021

In this study, we prepared phosphorous flame-retarding coating solutions by mixing triphosphate (3 phosphonate), phytic acid (6 phosphonate), or ammonium polyphosphate (10 phosphonate) with boric acid as a crosslinking agent and acryl resin binder. Prepared phosphorous flame-retarding coating solutions were coated onto non-woven fabrics, respectively, to obtain high flame-retarding effects. These prepared flame-retardant non-woven fabrics were evaluated using smoke density standard test (ASTM E662), limit oxygen index standard test (ISO E622), and vertical burning standard test (UL 94). Their flame-retarding effects were affected by the number of phosphonate groups. Regardless of natural or synthetic binder resins, their effects showed the following order: ammonium polyphosphate > phytic acid > triphosphate. Natural hydrocarbon compounds were also examined to determine the possible retardancy of binder resins. Results showed that natural hydrocarbon binder resins could be used for preparing fire-retardant nonwoven fabrics.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.319-328
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• 2021

Purpose: Recently, a large social disaster has called for the need to diagnose social disaster safety, and the Ministry of Public Administration and Security calculates and publishes regional safety ratings such as regional safety index and national safety diagnosis every year. The existing safety diagnosis system uses equal intervals or normal distribution to grade risk maps in a uniform manner. Method: However, the equidistant technique can objectively analyze risk ratings, but there is a limit to classifying risk ratings when the distribution is skewed to one side, and the z-score technique has a problem of losing credibility if the population does not follow a normal distribution. Because the distribution of statistical data varies from indicator to indicator, the most appropriate rating should be applied for each data distribution. Result: Therefore, in this paper, we analyze the data of disaster indicators and present a comparison and suitable method for traditional equidistant and natural brake techniques to proceed with optimized grading for each indicator. Conclusion: As a result, three of the six new indicators were applied differently from conventional grading techniques

• Journal of Applied Biological Chemistry
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• v.65 no.3
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• pp.195-202
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• 2022

Soybean (Glycine max L.) leaves have been researched as functional food stuff actively, but there is no validation method to control quality of soybean leaves (SL). In this study, we annotated seven kaempferol derivatives to confirm camelliaside A as index metabolite in SL using UHPLC-ESI-TOF-MS. HPLC-UVD validation method of camelliaside A in hot-water extract of SL was established according to validation guideline of functional foods from the Ministry of Food and Safety of Korea. The HPLC-UVD method was validated with reliable parameters for examining specificity, accuracy, precision, limit of detection and quantification and linearity. The established method gave the suitable ranges to qunatitate camelliaside A from the hot-water extract of soybean leaves.

• Textile Coloration and Finishing
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• v.33 no.4
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• pp.250-257
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• 2021

Laundry industry has traditionally been considered an industry that generates large amounts of wastewater and Volatile Organic Compounds(VOCs). This is still the case until now. Household laundry detergents are produced and distributed within the safety regulations on the amount of harmful substances detected. While industrial laundry detergents are often distributed without safety regulations, and even laundry workers manufacture and use them on their own. This contaminates water and air and also threatens the safety of workers. This study is a basic study for distributing eco-friendly detergents(EFD-A) developed through previous studies to the laundry industry. Safety, washability and wastewater quality of EFD-A are evaluated. Three existing commercial detergents(PD1, PD2, LD4) are also evaluated to compare with EFD-A. The safety of detergents is confirmed by the content of optical brightener, VOCs, and arsenic. Washability is evaluated by the difference in reflectance of washed and unwashed artificial soiled fabrics according to detergent concentration, washing temperature, and washing time. TOC is used as the index of assessing the wastewater quality. The results are as follows; EFD-A doesn't contain the optical brighteners, VOCs, and arsenic. The optimal washing conditions for EFD-A are 3 g/L concentration, 40 ℃ washing temperature, and 30 min washing time. The soil removal efficiency is about 71 %, which was similar to or somewhat superior to that of PD1, PD2, and LD4. TOC is 63.5 %, which is about 15 % lower than the discharge limit. Through this study, the developed detergent EFD-A can be used as a safe and eco-friendly detergent for the human body and the environment.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Korean Journal of Environmental Agriculture
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• v.41 no.4
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• pp.261-275
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• 2022

BACKGROUND: Prior to implementing a positive list system (PLS), there is a need to establish acceptable daily intake (ADI) and maximum residue limit (MRL) for veterinary drugs that have been approved a few decades ago in South Korea. On top of that, chronic dietary exposure assessment of veterinary drug residues should be performed to determine whether the use of these veterinary drugs would cause health concerns or not. METHODS AND RESULTS: To establish the ADI, the relevant toxicological data were collected from evaluation reports issued by international organizations. A slightly modified global estimate of chronic dietary exposure (GECDE) model was employed in the exposure assessment owing to the limited residual data. Therefore, only the ADI of ephedrine was established due to insufficient data for the other veterinary drugs. Thus, instead of ADI, the threshold of toxicological concern (TTC) value was used for the other drugs. Lastly, the hazard index (HI) was calculated, except for etizazole hydrochloride, due to the potential of mutagenicity. CONCLUSION(S): The HI values of ephedrine, menichlopholan, and anacolin were found to be as high as 6.4%, suggesting that chronic dietary exposure to the residues from these uses was unlikely to be a public health concern. Further research for exposure assessment of veterinary drug residues should be performed using up-todate Korean national health and nutrition examination survey (KNHANES) food consumption data. In addition, all relevant available data sources should be utilized for identifying the potentials of toxicity.

• The Journal of Information Systems
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• v.32 no.1
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• pp.179-199
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• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.