• Journal of Food Hygiene and Safety
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• v.36 no.6
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• pp.474-480
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• 2021

Sodium alginate is the sodium salt of alginic acid, commonly used as a food additive for stabilizing, thickening, and emulsifying properties. A relatively simple and universal analysis method is used to study sodium alginate due to the complex pretreatment process and extended analysis time required during the quantitative method. As for the equipment, HPLC-UVD and Unison US-Phenyl column were used for analysis. For the pretreatment condition, a shaking apparatus was used for extraction at 150 rpm for 180 minutes at room temperature. The calibration curve made from the standard sodium alginate solution in 5 concentration ranges showed that the linearity (R²) is 0.9999 on average. LOD and LOQ showed 3.96 mg/kg and 12.0 mg/kg, respectively. Furthermore, the average intraday and inter-day accuracy (%) and precision (RSD%) were 98.47-103.74% and 1.69-3.08% for seaweed jelly noodle samples and 99.95-105.76% and 0.59-3.63% for sherbet samples, respectively. The relative uncertainty value was appropriate for the CODEX standard with 1.5-7.9%. To evaluate the applicability of the method developed in this study, the sodium alginate concentrations of 103 products were quantified. The result showed that the detection rate is highest from starch vermicelli and instant fried noodles to sugar processed products.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.23-32
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• 2021

The high-level Space Situational Awareness (SSA) objective is to provide to the users dependable, accurate and timely information in order to support risk management on orbit and during re-entry and support safe and secure operation of space assets and related services. Therefore the risk assessment for the re-entry of space objects should be managed nationally. In this research, the Software for Re-Entry Prediction of space objects (SREP) was developed for national SSA system. In particular, the rate of change of the drag coefficient is estimated through a newly proposed Drag Scale Factor Estimation (DSFE), and is used for high-precision orbit propagator (HPOP) up to an altitude of 100 km to predict the re-entry time and position of the space object. The effectiveness of this re-entry prediction is shown through the re-entry time window and ground track of space objects falling in real events, Grace-1, Grace-2, Tiangong-1, and Chang Zheng-5B Rocket body. As a result, through analysis 12 hours before the final re-entry time, it is shown that the re-entry time window and crash time can be accurately predicted with an error of less than 20 minutes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.147-155
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• 2022

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1263-1263
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• 2001

The amounts of organic matter present in soil and the rate of soil organic matter (SOM) turnover are influenced by agricultural management practice, such as rotation, tillage, forage plow down direct seeding and manure application. The amount of nutrients released from SOM is highly dependent upon the state of the organic matter. If it contains a large proportion of light fractions (low-density) more nutrients will be available to the glowing crops. However, if it contains mostly heavy fractions (high-density) that are difficult to breakdown, then lesser amounts of nutrients will be available. The state of the SOM and subsequent release of nutrients into the soil can be predicted by NIRS as long as a robust regression equation is developed. The NIRS method is known for its rapidity, convenience, simplicity, accuracy and ability to analyze many constituents at the same time. Our hypothesis is that the NIRS technique allows researchers to investigate fully and in more detail each field for the status of SOM, available moisture and other soil properties in Alberta soils for precision farming in the near future. One hundred thirty one (131) Alberta soils with various levels (low 2-6%, medium 6-10%, and high >10%) of organic matter content and most of dry land soils, including some irrigated soils from Southern Alberta, under various management practices were collected throughout Northern, Central and Southern Alberta. Two depths (0- 15 cm and 15-30 cm) of soils from Northern Alberta were also collected. These air-dried soil samples were ground through 2 mm sieve and scanned using Foss NIR System 6500 with transport module and natural product cell. With particle size above 150 microns only, the “Ludox” method (Meijboom, Hassink and van Noorwijk, Soil Biol. Biochem.27: 1109-1111, 1995) which uses stable silica, was used to fractionate SOM into light, medium and heavy fractions with densities of <1.13, 1.13-1.37 and >1.37 respectively, The SOM fraction with the particle size below 150 microns was discarded because practically, this fraction with very fine particles can't be further separated by wet sieving based on density. Total organic matter content, mechanical texture, ash after 375$^{\circ}C$, and dry matter (DM) were also determined by “standard” soil analysis methods. The NIRS regression equations were developed using Infra-Soft-International (ISI) software, version 3.11.

• Journal of the Korean Geotechnical Society
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• v.25 no.2
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• pp.45-54
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• 2009

The electrical characteristics of soils have been used for investigating soil properties. The purpose of this study is the development and application of the electrical resistivity cone probe (ERCP) for the evelation of the porosity in the field with high precision. The shape of the probe tip is a cone shape to minimize the disturbance during penetration. In addition, the four terminal pair configuration is adopted to minimize the electrical interference. The electrical resistances are continuously measured during penetration of the ERCP using penetration rigs with 0.33 mm/sec penetration rate at Incheon and Busan sites. With the measured resistance profile and electrical resisivity of electrolyte of undisturbed samples, soil porosity profiles are obtained by using Archie's law. The empirical coefficients for the Archie's law are obtained based on the electrolyte extracted from the undisturbed samples. The estimated porosity profiles show similar trends to those of in-situ penetration tests such as SPT, CPT, and DMT. This study suggests that the ERCP may be an effective tool for the porosity estimation in the field with minimum disturbance.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.173-191
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• 2022

In this study, the shear wave velocity of the ground was measured using Flat TDR, and the precision analysis of the measured value and the verification of field applicability were performed. The shear wave velocity measurement value was derived in the field using the piezo-stack combined in the Flat TDR. analyzed. As a result of the experiment, the average value of the change in shear wave speed at the time of grout material injection was 10.15 m/s at the beginning of age, and the average value of the change in shear wave speed after the 7th to 14th days was 65.99 m/s, showing a tendency to increase with age. Also, it was found that dry density and shear wave speed increased as the water content increased on the dry side, and that the dry density and shear wave rate decreased as the water content increased on the wet side as the water content increased. The shear modulus value derived from the field test was confirmed to be a minimum of 17.36 MPa and a maximum of 28.13 MPa, confirming a measurement value similar to the reference value. Through this, it can be seen that the measured value of the shear modulus using Flat TDR is reliable data, and it can be determined that the compaction management of the site can be effectively managed in the future.

• Analytical Science and Technology
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• v.20 no.4
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• pp.347-354
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• 2007

A simple and rapid high-performance liquid chromatography assay for the determination of residual novobiocin levels in bovine, porcine, chicken, flatfish and japanese eel muscle has been developed and validated. The separation condition for HPLC/UV was optimized with phenyl hexyl ($4.6{\times}150mm$, $5{\mu}m$) column with 10 mM monobasic sodium phosphate buffer (pH 2.5)/acetonitrile (50/50, v/v) as the mobile phase at a flow rate of 1.0 mL/min and detection wavelength was set at 254 nm. Residues were extracted from tissue by blending with methanol and lipid materials were removed with n-hexane. Then, the methanol extract was evaporated to dryness under a nitrogen stream, reconstituted in the mobile phase. Aliquot of the organic extract was decanted and filtered through $0.45{\mu}m$ syringe filter. The $20{\mu}L$ of the resulting solution was injected into the HPLC system. The calibration ranges were $0.5{\sim}5{\mu}g/g$ and calibration curves were linear with coefficients of correlation better than 0.95. The limits of quantification were $0.5{\mu}g/g$ for all muscles. The recoveries of bovine, porcine, chicken, flatfish and japaneseel muscles were 99.8%, 102.4%, 91.0%, 104.0% and 93.0%, respectively. The procedures were validated according to the CODEX guideline, determining specificity, linearity, accuracy, precision, quantitation limit and recovery.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.102-110
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• 2023

PSC-I girder bridges constitute the largest proportion among highway bridges in Korea. According to the precision safety diagnosis data for the past 10 years, approximately 41.3% of the PSC-I bridges have been graded as C. Furthermore, with the increase in the aging of bridges, preemptive management is becoming more important. Damage and deterioration to the deck and girder with a long replacement cylce can have considerable impacts on the service and deterioration of a bridge. In addition, the high rate of device damages, including expansion joints and bearings, necessitates an investigation into the influence of the device damage in the structural members of the bridge. Therefore, this study defined representative PSC-I girder bridges with single and multiple spans to evaluate heterogeneous damages that incorporate the damage of the bridge member and device with the deterioration of the deck. The heterogeneous damages increased a crack area ratio compared to the individual single damage. For the single-span bridge, the occurrence of bearing damage leads to the spread of crack distribution in the girder, and in the case of multi-span bridges, expansion joint damage leads to the spread of crack distribution in the deck. The research underscores that bridge devices, when damaged, can cause subsequent secondary damage due to improper repair and replacement, which emphasizes the need for continuous observation and responsive action to the damages of the main devices.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.