• Mass Spectrometry Letters
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• v.10 no.2
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• pp.43-49
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• 2019

The aim of this study was conducted to develop an analytical method to determine the concentration of ceftiofur residue in eel, flatfish, and shrimp. For derivatization and extraction, the sample was hydrolyzed with dithioerythritol to produce desfuroylceftiofur, which was then derivatized by iodoacetamide to obtain desfuroylceftiofur acetamide. For purification, the process of solid phase extraction (Oasis HLB) was used. The target analytes were confirmed and quantified in $C_{18}$ column using liquid chromatography-tandem mass spectrometry with 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) as the mobile phase. The linearity of the standard calibration curve was confirmed by a correlation coefficient, $r^2>0.99$. The limit of quantification for ceftiofur was 0.002 mg/kg; the accuracy (expressed as the average recoveries) was 80.6-105%; the precision (expressed as the coefficient of variation) was below 6.3% at 0.015, 0.03, and 0.06 mg/kg. The validated method demonstrated high accuracy and acceptable sensitivity to meet the Codex guideline requirements. The developed method was tested using market samples. As a results, ceftiofur was detected in one sample. Therefore, it can be applied to the analysis of ceftiofur residues in fishery products.

• Analytical Science and Technology
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• v.32 no.2
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• pp.56-64
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• 2019

Recently, a number of adulterated products, which are advertised as hair-growth enhancer have been emerged among those who suffer hair loss disease. For continuous control of illegal products, in this study, a rapid and sensitive method for simultaneous screening of 12 compounds that enhance hair-growth was established to protect public health by ultrahigh-performance liquid chromatography coupled to quadrupole-orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS). Fragmentation pathways of them were proposed based on $MS^2$ spectral data obtained using the established method. In this analysis, the LODs and LOQs ranged from 0.05 to 50 ng/mL and from 0.17 to 167 ng/mL, respectively. The square of the linear correlation coefficient ($R^2$) was determined as more than 0.995. The intra- and inter-assay accuracies were respective 88-112 % and 88-115 %. Their precision values were measured within 5 % (intra-day) and 10 % (inter-day). Mean recoveries of target compounds in adulterated products ranged from 84 to 115%. The relative standard deviation of stability was less than 12 % at $4^{\circ}C$ for 48 h. The method was employed to screen 14 dietary supplements advertised to be effective for the treatment of hair loss. Some of the products (~21 %) were proven to contain synthetic drugs that promote hair growth such as triaminodil, minoxidil, and finasteride.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.323-327
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• 2021

This paper produced SUS MASK, which is used for OLEDs, using an automatic liquid management system. The SUS MASK was tested by setting the hole diameter to 0.4 mm. The additive F300 was found to be excellent as the hole diameter was close to 0.4 mm and the error range was measured to be 0.08 on average. And as a result of measuring the weight reduction amount of CuCl2 and FeCl3 according to the change in oxidation-reduction potential (ORP), FeCl3 is relatively sensitive to ORP changes. Experiments were conducted on whether ORP (610 mV) and specific gravity (1.463) were automatically controlled while continuously etching the SUS Mask. Experimental results show that the automatic liquid management system is well controlled because the setting value is not significantly changed. After setting the hole diameter to 0.4 mm as the target, the experiment results were measured from 0.36 to 0.44. Therefore, it is expected that etching processing in the manufacturing process of SUS MASK can be improved with higher precision by applying the manufactured automatic liquid management system.

• Journal of Environmental Health Sciences
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• v.48 no.2
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• pp.123-129
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• 2022

Background: The Environmental Health Action Program was a national project carried out from 2012~2021. It was aimed at developing public technologies to protect people's health from various environmental hazards. Objectives: One of the final goals of the project was "creating health benefits worth more than 179.2 billion won by reducing the environmental burden of disease." This study aims to evaluate whether the program sufficiently achieved the planned benefits. Methods: In order to secure consistency in evaluation, we applied the same equation used in the goal-setting process. It is comprised of six parameters to estimate the benefit: 1. The amount of medical expenses for environmental diseases; 2. The attributable proportion of environmental risk factors' 3. The rate of reduction in medical expenses for environmental diseases; 4. R&D project contribution; 5. The proportion of successful policy reflection; and 6. The contributions of the project. The corresponding variables were estimated at the end of the project, and the health benefits of the project were recalculated using the newly estimated variables. Results: It was estimated that a total of 195 billion won in health benefits occurred or will occur from 2015 to 2026. The main contributors for achieving the target were an increase in medical expenses for environmental diseases, a high score in the R&D project contribution, and the proportion of successful policy reflection. Conclusions: Technically, the equation used in the project is about medical expenses for environmental diseases rather than about the environmental burden of disease. There are several benefits of using the environmental burden of disease in the evaluation of public health policies. In further studies, developing a policy evaluation framework using indicators such as population attributable fraction would be needed.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.319-324
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• 2022

If radio interference occurs or there is no navigation device, radio navigation of high-speed moving object becomes impossible. Nevertheless, if there are multiple ground stations and precise range measurement between the high-speed moving object and the ground station can be secured, it is possible to estimate the position of moving object. This paper proposes a position estimation method using high-precision TDOA measurement generated using TLM signal. In the proposed method, a common error of moving object is removed using the TDOA measurements. The measurements is generated based on TLM signal including SOQPSK PN symbol capable of precise timing synchronization. Therefore, since precise timing synchronization of the system has been performed, the timing error between ground stations has a very small value. This improved the position estimation performance by increasing the accuracy of the measured values. The proposed method is verified through software-based simulation, and the performance of estimated position satisfies the target performance.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.312-318
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• 2022

Currently, the Republic of Korea is facing the problem of a decrease in military service resources due to the demographic cliff, and is pursuing military restructuring and changes in the military force structure in order to respond to this. In this situation, the Army is pushing forward the deployment of a drone-bot combat system that will lead the future battlefield. The battlefield of the future will be changed into an integrated battlefield concept that combines command and control, surveillance and reconnaissance, and precision strike. According to these changes, unmanned combat system, including dronebots, will be widely applied to combat situations that are high risk and difficult for humans to perform in actual combat. In this paper, as one of the countermeasures to these changes, autonomous behavior software with a BDI architecture-based decision-making system was developed. The autonomous behavior software applied a framework structure to improve applicability to multiple models. Its function was verified in a PC-based environment by assuming that the target UAV is a battalion-level surveillance and reconnaissance UAV.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.151-162
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• 2022

Recently, the forests in Korea have accumulated damage due to continuous forest disasters, and the need for technologies to monitor forest managements is being issued. The size of the affected area is large terrain, technologies using drones, artificial intelligence, and big data are being studied. In this study, a standard dataset were conducted to develop an algorithm that automatically detects suspicious trees damaged by forest pests using deep learning and drones. Experiments using the YOLO model among object detection algorithm models, the YOLOv4-P7 model showed the highest recall rate of 69.69% and precision of 69.15%. It was confirmed that YOLOv4-P7 should be used as an automatic detection algorithm model for trees suspected of being damaged by forest pests, considering the detection target is an ortho-image with a large image size.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• Journal of radiological science and technology
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• v.46 no.2
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• pp.141-149
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• 2023

The Korea Institute of Radiological and Medical Sciences plans to produce ²²⁵Ac, a therapeutic radio-pharmaceutical for precision oncology, such as prostate cancer. Radon, a radioactive gas, is generated by radium, the target material for producing ²²⁵Ac. The radon concentration is expected to be about 2000 Bq·m^-3. High-concentration radon-generating facilities must meet radioactive isotope emission standards by lowering the radon concentration. However, most existing studies concerning radon removal using activated carbon filters measured radon levels at concentrations lower than 1000 Bq·m^-3. This study measured ²²²Rn removal of coconut-based activated carbon filter under a high radon concentration of about 2000 Bq·m^-3. The ²²²Rn removal efficiency of activated carbon impregnated with triethylenediamine was also measured. As a result, the ²²²Rn removal amount of the activated carbon filter showed sufficient removal efficiency in a ²²²Rn concentration environment of about 2000 Bq·m^-3. In addition, despite an expectation of low radon reduction efficiency of Triethylenediamine-impregnated activated carbon, it was difficult to confirm a significant difference in the results. Therefore, it is considered that activated carbon can be used as a radioisotope exhaust filter regardless of whether or not Triethylenediamine is impregnated. The results of this study are expected to be used as primary data when building an air purification system for radiation safety management in facilities with radon concentrations of about 2000 Bq·m^-3.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.17-24
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• 2022

Mobile malicious apps are increasing rapidly, and Android, which accounts for most of the global mobile OS market, is becoming a major target of mobile cyber security threats. Therefore, in order to cope with rapidly evolving malicious apps, there is a need for detection techniques of malicious apps using machine learning, one of artificial intelligence implementation technologies. In this paper, we propose a selected feature method using feature selection and feature extraction that can improve the detection performance of malicious apps. In the feature selection process, the detection performance improved according to the number of features, and the API showed relatively better detection performance than the permission. Also combining the two characteristics showed high precision of over 93% on average, confirming that the appropriate combination of characteristics could improve the detection performance.