• Analytical Science and Technology
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• v.34 no.1
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• pp.23-35
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• 2021

In order to measure the volatile organic compounds (VOCs) of a sample which is too large to use commercially available chamber, a stainless steel vacuum chamber (VC) (with an internal diameter of 205 mm and a height of 50 mm) was manufactured and the temperature of the chamber was controlled using an oven. After concentrating the volatiles of the sample in the chamber by helium gas, it was made possible to remove residual volatile substances present in the chamber under reduced pressure ((2 ± 1) × 10-2 mmHg). The chamber was connected to a purge & trap (P&T) using a 6 port valve to concentrate the VOCs, which were analyzed by gas chromatography-mass spectrometry (GC-MS) after thermal desorption (VC-P&T-GC-MS). Using toluene, the toluene recovery rate of this device was 85 ± 2 %, reproducibility was 5 ± 2 %, and the detection limit was 0.01 ng L-1. The method of removing VOCs remaining in the chamber with helium and the method of removing those with reduced pressure was compared using Korean drinking water regulation (KDWR) VOC Mix A (5 μL of 100 ㎍ mL-1) and butylated hydroxytoluene (BHT, 2 μL of 500 ㎍ mL-1). In case of using helium, which requires a large amount of gas and time, reduced pressure ((2 ± 1) × 10-2 mmHg) only during the GC-MS running time, could remove VOCs and BHT to less than 0.1 % of the original injection concentration. As a result of analyzing volatile substances using VC-P&T-GC-MS of six types of cell phone case, BHT was detected in four types and quantitatively analyzed. Maintaining the chamber at reduced pressure during the GC-MS analysis time eliminated memory effect and did not affect the next sample analysis. The volatile substances in a cell phone case were also analyzed by dynamic headspace (HT3) and GC-MS, and the results of the analysis were compared with those of VC-P&T-GC-MS. Considering the chamber volume and sample weight, the VC-P&T configuration was able to collect volatile substances more efficiently than the HT3. The VC-P&T-GC-MS system is believed to be useful for VOCs measurement of inhomogeneous large sample or devices used inside clean rooms.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.34-41
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• 2021

The purpose of this research is to improve analysis methods of determining the contents of fatty acids in infant formulas and follow-up formulas. A gas chromatography (GC) method was performed on a GC system coupled to flame ionization detector, with a fused silica capillary column (SP2560, 100 m×0.25 mm, 0.20 ㎛). The method was validated using standard reference material (SRM, NIST 1849a). Performance parameters for method validation such as specificity, linearity, limits of detection (LOD) and quantification (LOQ), accuracy and precision were examined. The linearity of standard solution with correlation coefficient was higher than 0.999 in the range of 0.1-5 mg/mL. The LOD and LOQ were 0.01-0.06 mg/mL and 0.03-0.2 mg/mL, respectively. The recovery using standard reference material was confirmed and the precision was found to be between 0.8% and 2.9% relative standard deviation (RSD). Optimized methods were applied in sample analysis to verify the reliability. All the tested products had acceptable contents of fatty acids compared with component specification for nutrition labeling. The result of this research will provide efficient experimental information and strengthen the management of nutrients in infant formula and follow-up formula.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.487-497
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• 2021

The purpose of this study is to identify factors affecting length of stay(LOS) and death in tuberculosis(TB) patients by disease type, patient characteristic, admission and disease characteristic, and hospital characteristic from 2008 to 2017. Survey data was using Korean national hospital discharge in-depth survey data produced by Korea Disease Control and Prevention Agency. Study subjects were 10,634 inpatients with TB(A15, A16, A17, A18, A19, U88.0, U88.1, U84.30, U84.31) and analyzed frequency, chi-square test, Fisher's exact test, and logistic regression by using STATA 13.0. As a study result, the type of TB(extrapulmonary TB, multidrug-resistant TB, extensively drug-resistant TB), sex(woman), age(35-49, 50-64, 65-74, 75 years old or older), admission type(outpatient department), CCI(1-2 point, 3 point over), hospital location(metropolitan city) and bed size(300-499, 500-999, over 1000) were significantly influence LOS. Also, the type of TB(extrapulmonary TB, extensively drug-resistant TB), sex(woman), age(50-64, 65-74, 75 years old or older), residence(small town/rural), admission type(outpatient department), CCI(1-2 point, 3 point over), hospital location(provincial) were significantly influence death. In conclusion, the existing tuberculosis management has been patient management with rapid diagnosis and treatment following early detection. But other studies should be carried out for the system that identifies and supports high-risk groups of the long-term length of stay in hospital or high mortality rates as a result of treatment.

• CELLMED
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• v.11 no.3
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• pp.13.1-13.9
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• 2021

Background and Objective: Nux-vomica based traditional Unani formulation, Habb-e-Azaraqi (HAZ) is an important drug used by Unani physicians since several decades. It possesses Muqawwi-i-A'sab (nervine tonic), Muharrik-i-A'sab (nervine stimulant) properties and is an effective treatment option for diseases like Laqwa (facial palsy), Falij (paralysis), Niqris (gout) and Waja'al-Mafasil (arthritis) etc. The aim of the study is to access and provide information of HAZ for its TLC, HPTLC Fingerprinting defining its clear qualitative perspective and acute oral toxicity evaluation for its safety assessment which was not done earlier, thus contributing in the field of research. Materials and Methods: The chief ingredient, nux-vomica was detoxified as per method mentioned in Unani Pharmacopeia before its use in formulation. TLC and HPTLC was developed under four detection system i.e., UV 366nm, UV 254nm, exposure to iodine vapours and after derivatization with anisaldehyde sulphuric acid. Acute toxicity studies were performed as per OECD Guidelines 425 at a limit dose of 2000 mg/kg. Observations were done for signs of toxicity, body weight, and feed consumption at regular intervals followed by haematological and biochemistry evaluation. Results: The generated data proved the authenticity and established the TLC and HPTLC profile of the formulation. Acute toxicity revealed no significant differences in HAZ-treated animals with respect to body weight gain, feed consumption, haematology, clinical biochemistry evaluation. No significant gross pathological observation was noticed in necropsy. Conclusion: Data of the present study is substantial and scientific proof of HAZ in terms of standardization and toxicity study that can be utilize in future research activities.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.244-247
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• 2022

In this paper, the performance of the decoding schemes using linear MMSE filters in the frequency and time domains and the reinforcement Gauss-Seidel algorithm for the orthogonal time frequency space (OTFS) system that can improve robustness under high-speed mobile environments are compared. The reinforcement Gauss-Seidel algorithm can improve the bit error rate performance by suppressing the noise enhancement. The simulation results show that the performance of the decoding scheme using the linear MMSE filter in the frequency domain is severely degraded due to the effect of Doppler shift as the mobile speed increases. In addition, the decoding scheme using the reinforcement Gauss-Seidel algorithm under the channel environment with 120 km/h and 500 km/h speeds outperforms the decoding schemes using linear MMSE filters in the frequency and time domains.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.515-520
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• 2022

In context awareness using multiple sensors, when using sensor data detected and sent by each sensor, it is necessary to give different weights for each sensor. Even if the same type of sensor is configured for the same situation, sometimes it is necessary to assign different weights due to other secondary factors. It is inevitable to assign weights to events in the real world, and it can be said that a weighting method that can be used in a context awareness system using multiple sensors is necessary. In this study, we propose a weighting method for each sensor that reports to the host while the sensors continue to detect over time. In most IoT environments, the sensor continues the detection activity, and when the detected value shows a change pattern beyond a predetermined range, it is basically reported to the host. This can be called a kind of data stream environment. A weighting method was proposed for sensing data from multiple sensors in a data stream environment, and the new weighting method was to select and assign weights to data that indicates a context change in the stream.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.1-9
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• 2022

Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.

• Journal of The Korean Astronomical Society
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• v.55 no.5
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• pp.173-194
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• 2022

We complete the survey for finite-source/point-lens (FSPL) giant-source events in 2016-2019 KMTNet microlensing data. The 30 FSPL events show a clear gap in Einstein radius, 9 𝜇as < 𝜃_E < 26 𝜇as, which is consistent with the gap in Einstein timescales near t_E ~ 0.5 days found by Mróz et al. (2017) in an independent sample of point-source/point-lens (PSPL) events. We demonstrate that the two surveys are consistent. We estimate that the 4 events below this gap are due to a power-law distribution of free-floating planet candidates (FFPs) dN_FFP/d log M = (0.4 ± 0.2) (M/38 M_⊕)^-p/star, with 0.9 ≲ p ≲ 1.2. There are substantially more FFPs than known bound planets, implying that the bound planet power-law index 𝛾 = 0.6 is likely shaped by the ejection process at least as much as by formation. The mass density per decade of FFPs in the Solar neighborhood is of the same order as that of 'Oumuamua-like objects. In particular, if we assume that 'Oumuamua is part of the same process that ejected the FFPs to very wide or unbound orbits, the power-law index is p = 0.89 ± 0.06. If the Solar System's endowment of Neptune-mass objects in Neptune-like orbits is typical, which is consistent with the results of Poleski et al. (2021), then these could account for a substantial fraction of the FFPs in the Neptune-mass range.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.