• 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 Environmental Agriculture
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• v.41 no.2
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• pp.108-114
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• 2022

BACKGROUND: This study was performed to determine residual characteristics of soil-treated metalaxyl-M and dinotefuran in crown daisy and to evaluate the risks from intake of the residual pesticides in the crop. METHODS AND RESULTS: The pesticide granules were treated in soil on two levels, and the plants samples were collected 51 days after seeding. The analytes were extracted and partitioned using the QuEChERS extraction packet (MgSO₄ 4 g, NaCl 1 g). The quantitative methods for metalaxyl-M and dinotefuran were validated in linearity, accuracy, and precision. Risk assessments of the pesticides were performed using Korea national nutrition statistics 2019. CONCLUSION(S): The residual concentrations of metalaxyl-M in crown daisy were 0.09-0.10 mg/kg (for the treatment at 6 kg/10 a) and 0.17-0.19 mg/kg (12 kg/10 a), respectively. The residual concentrations of dinotefuran in the crop were 0.53-0.75 mg/kg (3 kg/10 a) and 1.17-1.26 mg/kg (6 kg/10 a). The amounts of pesticides were less than MRL (Maximum Residue Limits) according to the Korean MFDS (Ministry of Food and Drug Safety). The HI (Hazard Index) of metalaxyl-M and dinotefuran for consumers was 0.0075% and 0.2250%, respectively. For females in the age between 50-64, the major consumer group, the HIs of the pesticides were <3%. Considering the consumption of crown daisy, they are not considered to be of toxicological concern.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.480-493
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• 2021

Since the generalized Hoek-Brown criterion (GHB) provides an efficient way of identifying its strength parameter values with the consideration of in-situ rock mass condition via Geological Strength Index (GSI), this criterion is recognized as one of the standard rock mass failure criteria in rock mechanics community. However, the nonlinear form of the GHB criterion makes its mathematical treatment inconvenient and limits the scope of its application. As an effort to overcome this disadvantage of the GHB criterion, the explicit approximate analytical equations for the minimum principal stress, which is associated with the maximum principal stress at failure, are formulated based on the Taylor polynomial approximation of the original GHB criterion. The accuracy of the derived approximate formula for the minimum principal stress is verified by comparing the resulting approximate minimum principal stress with the numerically calculated exact values. To provide an application example of the approximate formulation, the equivalent friction angle and cohesion for the expected plastic zone around a circular tunnel in a GHB rock mass are calculated by incorporating the formula for the approximate minimum principal stress. It is found that the simultaneous consideration of the values of mi, GSI and far-field stress is important for the accurate calculation of equivalent Mohr-Coulomb parameter values of the plastic zone.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.921-926
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• 2022

Sugar solutions (5%, 10%, 15% and 20%) were tested by seven sensors of Astree E-Tongue for selecting a sensor for sweetness. NMS sensor was chosen as a sensor for sweetness among two sensors (PKS and NMS sensors selected in first stage) by considering precision, linearity and accuracy. Sugar, fructose, glucose and xylitol (5%, 10% and 15%) were tested by E-tongue. The principal component analysis (PCA) result by E-Tongue with seven sensors at 5% concentration level of four sweetners was not satisfactory (Discrimination index was -0.1). On the other hand, the relative NMS sensor response values were derived as 1.08 (fructose), 0.99 (glucose) and 1.00 (xylitol) comparing to sugar. Only the E-Tongue relative glucose response 0.99 was different from 0.5~0.75 of the relative sweetness range reported as the human sensory test results. Considering the excellent precision (%RSD, 1.53~3.64%) of E-Tongue using NMS single sensor for three types of sweeteners compared to sugar in the concentration range of 5% to 15%, replacing sensory test of sweetened beverages by E-Tongue might be possible for new product development and quality control.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.12-36
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• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.