• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.533-541
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• 2022

An analysis of marine accidents that occurred in the last five years, revealed that 77.0 % of all grounding accidents and 66.1% of all marine casualties involved small vessels, which was a very high level relatively. The Mokpo Regional Maritime Safety Tribunal (Mokpo-KMST) inquired on 72 cases of marine accidents in 2021, of which 10 cases were grounding accidents. Furthermore, eight cases of grounding accidents occurred in small fishing vessels. This study analyzed eight cases of grounding accidents on small fishing vessels that inquired in the jurisdictional area of Mokpo-KMST in 2021. I found out that this grounding occurred in clear weather with good visibility (2-4 miles) and good sea conditions with a wave height of less than 1 meter. Furthermore, I found that the main causes of grounding were drowsy navigation due to fatigue, neglect of vigilance, neglect of checking ship's position, overconfidence in GPS plotter, and lack of understanding of chart symbols and tidal differences. To reduce grounding accidents of small fishing vessels, I suggested the following measures. First, crew members who have completed the able seafarer training course on bridge watchkeeping should assist to the master. Second, alarm systems to prevent drowsiness should be installed in the bridge. Third, the regulation should be prepared for the performance standards and updating GPS plotter. Finally, the skipper of small vessels should be trained periodically to be familiar with chart symbols and basic terrestrial navigation.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.167-188
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• 2023

This study reviewed the current status of low-experienced teacher librarians with less than 5 years and attempted to identify their educational needs through IPA analysis, Borich Priority Formula, and The Locus for Focus Model. A survey was conducted on low-experienced teacher librarians with less than 5 years of experience to analyze their process in the pre-service teacher training and experiences before an appointment and to identify teacher librarians instructional expertise. The results of the analysis of the study are as follows. First, there was a statistically significant difference between the importance and performance in all areas of instructional expertise of low-experienced teacher librarians. Second, 'reading education-practice progress' was recognized as a 'Keep up good work' with high importance and satisfaction, and 'library-based instruction planning, progress evaluation', 'information literacy-curriculum design', and 'digital and media literacy education-progress and evaluation' were recognized as areas of 'Concentrate here' through IPA analysis. Third, In the Borich Priority Formula, 'teaching-learning evaluation', 'teaching-learning progress', and 'teaching-learning plan' in the Library based instruction area showed the highest educational needs. Fourth, the library-based instruction was shown to the high discrepancy/high importancy area as same as the Borich Proity Formula. The results of this study can provide implications for improving the instructional expertise of teacher librarians.

• The Korea Journal of Herbology
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• v.28 no.6
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• pp.119-127
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• 2013

Objectives : ${\beta}$-Asarone (BAS) is an active ingredient in Acori Rhizoma. This study investigated anti-neuroinflammatory and memory ameliorating effects of BAS in systemic lipopolysaccharide (LPS)-treated C57BL/6 mice. Methods : BAS was administered orally at doses of 7.5, 15, and 30 mg/kg for 3 days prior to LPS (3 mg/kg, intraperitoneal) injection. Pro-inflammatory cytokine mRNA, including tumor necrosis factor-${\alpha}$ (TNF-ㅍ), interleukin (IL)-$1{\beta}$ and IL-6, was measured in hippocampus tissue using real-time polymerase chain reaction at 4 h after the LPS injection. An ameliorating effect of 30 mg/kg BAS on learning and memory impairment in the LPS-treated mice was verified using the Morris water maze test. Results : BAS significantly attenuated up-regulation of TNF-${\alpha}$, IL-$1{\beta}$, and IL-6 mRNA in hippocampus tissue of the LPS-treated mice. In acquisition training test, BAS improved learning performance of the LPS-treated mice with a significant decrease of escape latency to the platform. In memory retention test, BAS also ameliorated memory impairment of the LPS-treated mice with a significant increase of swimming time in zones neighboring to the platform, number of target heading, and memory score. Conclusion : The results suggest that inhibition of pro-inflammatory cytokines and neuroinflammation in the hippocampus by BAS could be one of the mechanisms for BAS-mediated ameliorating effect on learning and memory impairment in LPS-treated mice.

• 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.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.