• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• 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.4
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• pp.281-288
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• 2021

This study was conducted to analyze and compare the concentrations of heavy metals in 430 different products of 20 types of herbal medicines available in the domestic market in Korea by Graphite Furnace-Atomic Absorption Spectrometry (GF-AAS) and automated mercury analyzer. The accuracy for lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg) was in the range 92.67-102.56%, and the precision was 0.21-6.00 relative standard deviation (RSD%), which was in compliance with the Codex acceptable range. Furthermore, the Food Analysis Performance Assessment Scheme (FAPAS) quality control (QC) material showed a recovery range of 96.7-102.0% and 0.33-4.93 RSD%. The average contents (㎍/kg) of Pb, As, Cd, and Hg in herbal medicines were 254.9 (not detected (N.D.)-2,515.2), 171.0 (N.D.-2,465.2), 99.2 (N.D.-797.1), and 6.0 (N.D.-83.6), respectively. Based on the quantitative analysis results, the heavy metal contents of 20 types of herbal medicines distributed in Korea are within the acceptable range according to the standards issued by the Ministry of Food and Drug Safety (MFDS). By using the manufacturer of herbal products as the standard for QC, the Pb, As, Cd, and Hg contents were investigated in the packaging process just before distribution to determine the actual conditions of residual heavy metals in herbal medicines. Thus, these result may contribute to monitoring the QC of herbal medicines distributed in Korea and could provide basic data for supplying safe herbal medicines to the public.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.441-452
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• 2022

Training using metacognition in a learning environment is one of the topics that have been continuously studied since the 1990s. Metacognition can be broadly divided into declarative metacognitive knowledge and procedural metacognitive knowledge (metacognitive skills). Accordingly, metacognitive training has also been studied focusing on one of the two metacognitive knowledge. The purpose of this study was to examine the role of metacognitive skills training in the context of mathematical problem solving. Specifically, the learner performed the prediction of problem difficulty, estimation of problem solving time, and prediction of accuracy in the context of a test in which problems of various difficulty levels were mixed within a set, and this was repeated 5 times over a total of 5 weeks. As a result of the analysis, we found that there was a significant difference in all three predictive indicators after training than before training, and we revealed that training can help learners in problem-solving strategies. In addition, we analyzed whether there was a difference between the experiment group and control group in the degree of test anxiety and math achievement. As a result, we found that learners in the experiment group showed less emotional and relationship anxiety at 5 weeks. This effect through metacognitive skill training is expected to help learners improve learning strategies needed for test situations.

• Journal of IKEEE
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• v.25 no.4
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• pp.782-785
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• 2021

In this paper, we propose a personalized cooling management system with thermal imaging camera. The proposed equipment uses a thermal imaging camera to control the amount of cold air and the system according to the difference between the user's skin temperature before and after the procedure. When the skin temperature is abnormally low, the cold air supply is cut off to prevent the possibility of a safety accident. It is economical by replacing the skin temperature sensor with a thermal imaging camera temperature measurement, and it can be visualized because the temperature can be checked with the thermal image. In addition, the proposed equipment improves the sensitivity of the sensor that measures the distance to the skin by calculating the focal length by using a dual laser pointer for the safety of a personalized cooling management system to which a thermal imaging camera is applied. In order to evaluate the performance of the proposed equipment, it was tested in an externally accredited testing institute. The first measured temperature range was -100℃~-160℃, indicating a wider temperature range than -150~-160℃(cryo generation/USA), which is the highest level currently used in the field. In addition, the error was measured to be ±3.2%~±3.5%, which showed better results than ±5%(CRYOTOP/China), which is the highest level currently used in the field. The second measured distance accuracy was measured as below ±4.0%, which was superior to ±5%(CRYOTOP/China), which is the highest level currently used in the field. Third, the nitrogen consumption was confirmed to be less than 0.15 L/min at the maximum, which was superior to the highest level of 6 L/min(POLAR BEAR/USA) currently used in the field. Therefore, it was determined that the performance of the personalized cooling management system applied with the thermal imaging camera proposed in this paper was excellent.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.354-360
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• 2022

Metabolic disease is a disease with a prevalence of 26% in Korean, and has three of the five states of abdominal obesity, hypertension, hunger glycemic disorder, high neutral fat, and low HDL cholesterol at the same time. This paper links the consumer panel data of the Rural Development Agency(RDA) and the medical care data of the National Health Insurance Service(NHIS) to generate a classification model that can be divided into a metabolic disease group and a control group through food consumption characteristics, and attempts to compare the differences. Many existing domestic and foreign studies related to metabolic diseases and food consumption characteristics are disease correlation studies of specific food groups and specific ingredients, and this paper is logistic considering all food groups included in the general diet. We created a classification model using regression, a decision tree-based classification model, and a classification model using XGBoost. Of the three models, the high-precision model is the XGBoost classification model, but the accuracy was not high at less than 0.7. As a future study, it is necessary to extend the observation period for food consumption in the patient group to more than 5 years and to study the metabolic disease classification model after converting the food consumed into nutritional characteristics.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of Food Hygiene and Safety
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• v.37 no.5
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• pp.297-305
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• 2022

Antioxidants are food additives that extend the shelf life of food products by preventing lipid rancidity caused by active oxygen. They can either be naturally-derived or manufactured synthetically via chemical synthesis. In this study, method validation of five synthetic antioxidants, namely butylated hydroxyanisole, butylated hydroxytoluene, tertiary butylhydroquinone, propyl gallate, and disodium ethylenediaminetetraacetic acid, was performed using a high performance liquid chromatography-ultraviolet visible detector, and the method applicability was evaluated by analyzing foods containing antioxidants. The coefficient of determination (R²) average was 0.9997, while the limit of detection and limit of quantification were 0.02-0.53 and 0.07-1.61 mg/kg, respectively. The intra and inter-day accuracies and precisions were 83.2±0.7%-98.7±2.1% and 0.1%-5.7% RSD, respectively. Inter-laboratory validation for accuracy and precision was conducted using the Food Analysis Performance Assessment Scheme quality control material. The results satisfied the guidelines presented by the AOAC International. In addition, the expanded uncertainty was less than 16%, as recommended by CODEX. Consequently, to enhance public health safety, the results of this study can be used as basis data for evaluating the intake of synthetic antioxidants and assessing their risks in Korea.

• 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.