• Journal of the Korean Wood Science and Technology
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• v.32 no.5
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• pp.29-38
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• 2004

In this study, we investigated the thermal properties of corn-starch filled polybutylene succinate-adipate (PBS-AD) bio-composites. Thermal analysis (TA) is used to describe the analytical method for measuring the chemical property and weight loss of composite materials as a function of temperature. The thermal stability of corn-starch was lower than that of pure PBS-AD. As corn-starch loading increased, the thermal stability and degradation temperature of the bio-composites decreased and the ash content increased. It can be seen that the degree of compatibility and interfacial adhesion of the bio-composites decreased because of the increasing mixing ratio of the corn-starch. As the content of corn-starch increased, there was no significant change in the glass transition temperature (T_g) and the melting temperature (T_m) for the bio-composites. The storage modulus (E') and loss modulus (E") of the corn-starch flour filled PBS-AD bio-composites were higher than those of PBS-AD, because of the incorporation of corn-starch increased the stiffness of the bio-composites. At higher temperatures, the decreased storage modulus (E') of bio-composites was due to the increased polymer chain mobility of the matrix polymer. From these results, we can expect that corn-starch has potential as a reinforcing filler for bio-composites. Furthermore, we recommend using a coupling agent to improve the interfacial adhesion between corn-starch and biodegradable polymer.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.4
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• pp.321-330
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• 2022

This study attempted to establish an optimal multi-compound simultaneous analysis method that can secure reliable results for 15 - preservatives, 2 - sun screens and 1 - antioxidants of cosmetics using HPLC-PDA. Since the potential of hydrogen (pH) in the mobile phase affects the acid dissociation constant (pKa) of the preservatives, and the peak retention time shift and area change were observed. The peak separation condition was established by adjusting the pH to 0.1% H₃PO₄ addition (mL) when preparing the mobile phase. As a results of method validation, the linearity correlation coefficient (R²) of above 0.999 were obtained, and accuracy 87.9 ~ 101.1%, 0.1 ~ 7.6% precision for two types of cosmetics (cream and shampoo). It was found that the limit of detection (LOD) was 0.1 ~ 0.2 mg/kg and the limit of quantitation (LOQ) was 2.0 ~ 4.0 mg/kg. In addition, it was possible to simultaneously separate p-anisic acid, a natural compound that was difficult to separate in HPLC due to the small difference from methylparaben, a synthetic preservatives. Through this study, it will be effectively used to secure quality control and safety for compound that need restrictions on use cosmetics.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.471-482
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• 2022

This study aims to investigate the characteristics of wave generation in a deep ocean engineering basin and to develop a meta-model of the transfer function of the wavemaker that reflects the geometric characteristics of the deep ocean engineering basin. To this end, the two-dimensional frequency domain boundary element method was applied to achieve an efficient analysis that reflects the geometric characteristics of the deep ocean engineering basin. The developed numerical method was validated through comparison with the analytical solution. Numerical analyses were conducted for the boundary value problem of the wavemaker according to various periods and the positions of the movable bottom. The numerical results were used to investigate the effect of the geometric characteristics of the deep ocean engineering basin on the transfer function of the wavemaker, and the effect of depth on wave generation was checked by changing the position of the movable bottom. To efficiently utilize the various results of the boundary element method, a meta-model, an approximate model of the transfer function of the wave maker, was developed using a thin plate spline interpolation model. The validity of the developed meta-model was confirmed through a comparison of the results of the model tests.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1011-1020
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• 2022

Previous studies for the assessment of submarine groundwater discharge (SGD) were perfomed for areas where a large amount of SGD was observed. Newly developed assessment methods were proposed that was based on an analytic solution using sharp interface model. The proposed mathematical equations used the existing observed groundwater level and hydrogeological data of Jeju Island as input data. The quantitatively assessed FSGD values were compared to the basin-scale recharge estimation values in Seong-San area in eastern Jeju. As a result of the study, it was estimated that the amount of FSGD in the Seongsan area ranges from about 2.65 to 9.15% of the amount of areal-recharge. Through the analysis of the FSGD combined with the analytic model, it is to be provided as a scientific tool to establish a more reasonable coastal water resource management plan.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.103-110
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• 2022

The purpose of this study is to experimentally evaluate the stiffness and strength reduction according to the reinforcing bar details of the vertically divided reinforced concrete shear walls. To confirm the effect of reducing strength and stiffness according to vertical division, four real-scale specimens were fabricated and repeated lateral loading tests were performed. As a result of the experiment, it was confirmed that the strength and stiffness were decreased according to the vertical division. In particular, as the stiffness reduction rate is greater than the strength reduction rate, it is expected that safety against extreme strength can be secured when the load is redistributed according to vertical division. As a result of checking the crack pattern, a diagonal crack occurred in the wall subjected to compression control among the divided walls. It was confirmed that two neutral axes occurred after division, and the reversed strain distribution appeared in the upper part, showing the double curvature pattern. In future studies, it is necessary to evaluate the stiffness reduction rate considering the effective height of the wall, to evaluate additional variables such as wall aspect ratio, and to conduct analytical studies on various walls using finite element analysis.

• CELLMED
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• v.11 no.4
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• pp.21.1-21.9
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• 2021

Background: Unani System of Medicine (USM) has its origin to Greece. To ensure and develop the quality, authenticity of Unani drugs, standardization on modern analytical parameter is essential requirement for drugs. Objectives: The aimed of the present study was to develop a standard profile of "Qurṣ-e-Mafasil" by systematic study through authenticated ingredients, pharmacognostic identification followed by physicochemical, TLC, HPTLC fingerprinting analysis as per standard protocol. Material and Methods: In this study three batches of "Qurṣ-e-Mafasil" QM were prepared by standard method as per UPI had been followed by organoleptic properties of formulation such as appearance, color, odor, taste. Powder Microscopy and physicochemical studies were carried out such as Uniformity of weight, Friability, Disintegration time, hardness, LOD, ash vales and extractive values in like aqueous, alcohol & hexane. Further qualitative tests such as Thin-Layer Chromatography (TLC), and High-Performance Thin Layer Chromatography (HPTLC) studies were also carried out to develop fingerprint pattern of the alcoholic solvent extract of QM. Phytochemical screening was carried out in different solvent extracts such as alcoholic, aqueous and chloroform extracts to detect the presence phytoconstituents in the formulation QM. Heavy metals, Microbial Load Contamination and pesticidal residues were also determined. Results: Qurṣ-e-Mafasil showed tablet-like appearance, light brown colour, mild pungent odour and acrid taste. Uniformity of weight (mg), friability (rpm), and hardness (kg/cm) and disintegration time was ranged between (500 to 503), (0.0340 to 0.038), (8.40 to 8.67) and (4-5 minutes) respectively for the three batches. Loss in weight on drying at 105℃ was ranged between (8.3425 to 8.7346). Extracted values were calculated in distilled water ranged between (30.9091 to 31.4358), hexane (1.1419 to 1.4281), and alcohol (3.3352 to 3.3962). The ash values recorded were ranged between (3.7336 to 3.8378), and acid insoluble ash (0.5859 to 0.6112).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.367-374
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• 2022

This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.

• Journal of Environmental Health Sciences
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• v.48 no.6
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• pp.315-323
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• 2022

Background: Socioeconomical disadvantaged communities are more vulnerable to environmental chemical exposure and associated health effects. However, there is limited information on chemical exposure among vulnerable populations in Korea. Objectives: This study investigated chemical exposure among underprivileged populations. We measured urinary metabolites of phthalates in urban disadvantaged communities and investigated their correlations with residential environment factors and relative socioeconomic vulnerability. Methods: Urine samples were collected from 64 residents in a disadvantaged community in Seoul. A total of eight phthalate metabolites were analyzed by liquid chromatography-mass spectroscopy. Analytical method used by the Korean National Environmental Health Survey (KoNEHS) was employed. Covariate variance analysis and general linear regression adjusted with age, sex and smoking were performed. Results: Several phthalate metabolites, namely monomethyl phthalate (MMP), monoethyl phthalate (MEP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono-n-butyl phthalate (MnBP) had higher levels than those reported in the adults of 4th KoNEHS. Notably, the MnBP level was higher in the lower socioeconomic group (geometric mean [GM]=47.3 ㎍/g creatinine) compared to non-recipients (GM=31.9 ㎍/g creatinine) and the national reference level (GM=22.0, 28.2 and 32.2 ㎍/g creatinine for adults, 60's and 70's, respectively.). When age, sex and smoking were adjusted, MEP and MnBP were significantly increased the lower socioeconomic group than non-recipients (p=0.014, p=0.023). The lower socioeconomic group's age of flooring were higher than non-recipients, not statistically significant. Conclusions: These results suggest that a relatively low income and aged flooring could be considered as risk factors for increased levels of phthalate metabolites in socioeconomic vulnerable populations.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.