• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.6
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• pp.533-540
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• 2012

Rubber laminated bearings with lead core are highly affected by changes in temperature because key materials which are rubber and lead have temperature dependencies. In this study, two full scale LRB(D800, S=5) are manufactured and temperature dependency tests on shear properties are accomplished. The shear properties at the 3rd cycle are used at $-10^{\circ}C$, $0^{\circ}C$, $10^{\circ}C$, $20^{\circ}C$, $30^{\circ}C$, $40^{\circ}C$ respectively. The double shear configuration, simultaneously testing two pieces, is applied for compression shear test in order to minimize the friction effects due to the test machine, described in ISO 22762-1:2010. Characteristic strength, post-yield stiffness, effective stiffness, equivalent damping ratio are estimated and presented coefficient due to the temperature changes.

• Food Science and Preservation
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• v.23 no.6
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• pp.848-858
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• 2016

This study was conducted to determine the $F_0$-values of a retort machine at different locations and to evaluate the effects of these $F_0$-values on various quality characteristics of retorted Samgyetang samples. Samples were divided into three groups based on $F_0$-values-T1, 10~20; T2, 20~30; T3, >30. Mineral content in Samgyetang broth and breast meat mostly increased with increasing $F_0$-values. In general, the free amino acid values, hardness, and springiness, except for bone springiness, of Samgyetang decreased significantly at higher $F_0$-values. Protein content of meat and broth of the treated samples were significantly lower than that of the control. An increase in the digestion rate of meat and porridge, as well as the turbidity of the broth was observed in most of the treated samples with increasing $F_0$-values. With increasing $F_0$-values, the $L^*$ and $b^*$ values of meat and the $b^*$ values of broth tended to increase, while the $a^*$ value of broth increased significantly. Electronic nose analysis revealed different flavor patterns for samples treated at different $F_0$-values. For sensory traits, samples treated with higher $F_0$-values tended to receive lower evaluations. Particularly, the color and texture of T3 samples were lower than those of T1 and T2 samples. In conclusion, to improve the quality of Samgyetang, the efficiency and optimization of retort machines as well as the standardization of sterilization techniques are needed.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.4
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• pp.299-304
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• 2007

There are to be some cracks on the material degradation part or the stress concentration parts of the main members, which carry on over-loads, of structures. Because these cracks can be used to evaluate the structural health status, it is important to monitor the crack growth for maintaining the structural safety. In this study, the fiber Bragg grating sensor with a drop ball was developed as a sensor for crack growth detection of an existing crack. The crack growth detection sensor was constructed with three parts: a probe part, a wavelength controling light source and receiver part, and an impact part. The probe part was just formed with a fiber Bragg grating optical fiber The wavelength controling light source part was composed of a current supplying circuit, a DFB laser diode, and a TEC controling circuit for wavelength control. Also, the impact part was just implemented by dropping a steel ball. The performance of this sensor was confirmed by the experiments of the crack detection with an aluminum plate having one existing crack. According to these experiments, the difference of the sensor signal outputs was correlated with the crack length. So, it was confirmed that this sensor could be applied to monitor the crack growth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.647-654
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• 2016

In the defense industry, national security takes priority over economic sense and this has translated into high cost and long-term research and development. However, the exponential growth of technology and rapid changes in the security situation in recent years have resulted in a call for the development of systems at a low cost within a short period of time. In order to implement a modularization strategy in the field of defense, the introduction of line replaceable units in OO systems needs to be prioritized. This study selects six criteria following a literature review and prioritizes 11 modules for OO systems using the project evaluation method, Grey Relational Analysis (GRA). Based on the GRA results, the grey relational grades were derived as 0.83, 0.81 and 0.80 for the M11 (Main board), M8 (EMI module), M3 (Single board computer) modules, respectively. The cost and time of development is expected to be reduced in accordance with the grey relational grade. The results of this research could be utilized for decision making on adopting modularization in similar system development or product improvement programs (PIPs).

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.