• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.475-481
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• 2019

A splint is one of assisting devices for the disabled with hemiplegia or contracture and is manually made by an experienced expert. Heated thermoplastic materials are continuously fitted to the affected part. This traditional method has a possible risk of low-temperature burn, quality variance of the splint due to the proficiency of maker. etc. While various approaches has been made using 3D printing technology in order to redeem those disadvantages, they still carry high cost issues with 3D scanners or accuracy issues with manual measurement. This research begins with symmetrical characteristics of human body and focuses on the preliminary study for the possibility of splint manufacturing with 3D printing technology based on geometric characteristics of unaffected arm. 3D right and left forearm models of healthy male adults were created by photogrammetry software and a series of digital images in order to measure the circumference and cross-sectional area of the forearm models at every 20mm from the elbow. The circumference and cross-sectional area showed tolerable levels of differences between both sides within subjects; The circumference and cross-sectional area showed very strong correlations between both sides within subjects. From these findings, the possibility of splint manufacturing with 3D printing technology could be confirmed based on the geometric characteristics of unaffected side.

• Composites Research
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• v.32 no.5
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• pp.199-205
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• 2019

The composite material has the advantage that the fibers can be arranged in a desired direction and can be manufactured in one piece. However, micro voids can be formed due to micro air, moisture or improper curing temperature or pressure, which may cause the deterioration in mechanical strength. In this paper, the composite panels with different thicknesses were made by varying the curing pressure in an autoclave vacuum bag process and their microporosities were evaluated. Microporosity was measured by image analysis method, acid digestion method, and combustion method and their correlation with ultrasonic attenuation coefficient was analyzed. From the test results, it was found that the acid digestion method had the highest accuracy and the lower the curing pressure, the higher the microporosity and the ultrasonic attenuation coefficient. In addition, the microporosity and the ultrasonic attenuation coefficient were increased as the thickness of the composite panel was increased at the same curing pressure.

• Convergence Security Journal
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• v.17 no.5
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• pp.41-47
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• 2017

The Marine Environment Observation Information System supplies oceanographic information elements such as water temperature, chlorophyll, float, etc. based on satellite images to consumers. The data produced by the Korean marine environmental observatories are located in the coastal areas of Korea. But if the range is too far from a particular area of interest, due to distance or spatial constraints, the accuracy and up-to-dateness of the data can not be relied upon. Therefore, it is necessary to perform fusion and complex operation to solve the difference between the field observation and the marine satellite image. In this study, we develop a system that can process marine environmental information in the user's area of interest in the form of layered character (numeric) information considering the readability and light weight rather than the satellite image. In order to intuitively understand satellite image information, we characterize (quantify) the marine environmental information of the area of interest and we process the satellite image band values into layered characters to minimize the absolute amount of transmitted / received data. Also we study modular location-based interest information service method to be able to flexibly extend and connect interested items that diversify various observation fields as well as application technology to serve this.

• Journal of the Korean Wood Science and Technology
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• v.49 no.3
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• pp.213-225
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• 2021

In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared by hydrothermal carbonization and established a model for predicting the carbonization degree using near-infrared spectroscopy and partial least squares regression. The carbon content of the hydrothermally carbonized lignin at the temperature of 200 ℃ was higher by approximately 3 wt% than that of the untreated sample, and the carbon content tended to gradually increase as the heating time increased. Hydrothermal carbonization made lignin more carbon-intensive and more homogeneous by eliminating the microparticles. The discriminant and predictive models using near-infrared spectroscopy and partial least squares regression approppriately determined whether hydrothermal carbonization has been applied and predicted the carbon content of hydrothermal carbonized lignin with high accuracy. In this study, we confirmed that we can quickly and nondestructively predict the carbonization characteristics of lignin hydrochar manufactured by hydrothermal carbonization using a partial least squares regression model combined with near-infrared spectroscopy.

• Journal of Nutrition and Health
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• v.54 no.1
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• pp.116-128
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• 2021

Purpose: The extraction conditions for bioactive components from peanut shells, which is a byproduct of peanut processing, were optimized to enhance the total phenolic content (TPC, Y1), total flavonoid content (TFC, Y2), and 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (RSA, Y3). In addition, this study evaluated the anti-obesity effect of peanut shell extract. Methods: Optimization of ultrasonic-assisted extraction (UAE) was performed using a response surface methodology. The independent variables applied for extraction were time (X1: 5.0-55.0), temperature (X2: 26.0-94.0), and ethanol concentration (X3: 0.0%-99.5%). Quadratic regression models were derived based on the results of 17 experimental sets, and an analysis of the variance was performed to verify its accuracy and precision of the regression equations. Results: When evaluating the effects of independent variables on responses using statistically-based optimization, the independent variable with the most significant effect on the TPC, TFC, and RSA was the ethanol concentration (p = 0.0008). The optimal extraction conditions to satisfy all three responses were 35.8 minutes, 82.7℃, and 96.0% ethanol. Under these conditions, the inhibitory activities of α-glucosidase and pancreatic lipase by the extract were 86.4% and 78.5%, respectively. Conclusion: In this study, UAE showed superior extraction efficiency compared to conventional hot-water extraction in the extraction of polyphenols and bioactive materials. In addition, α-glucosidase and pancreatic lipase inhibitory effects were identified, suggesting that peanut shells can be used as effective antioxidants and anti-obesity agents in functional foods and medicines.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• 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.