• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• 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.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Journal of the Korea Computer Graphics Society
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• v.3 no.1
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• pp.57-67
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• 1997

The navigation of virtual space comes down to the manipulation of the virtual camera. The movement of the virtual cameras has 6 degrees of freedom. However, input devices such as mouses and joysticks are 2D. So, the movement of the camera that corresponds to the input device is 2D movement at the given moment. Therefore, the 3D movement of the camera can be implemented by means of the combination of 2D and 1D movements of the camera. Many of the virtual space navigation browser use several navigation modes to solve this problem. But, the criteria for distinguishing different modes are not clear, somed of the manipulations in each mode are repeated in other modes, and the kinesthetic correspondence of the input devices is often confusing. Hence the user has difficulty in making correct decisions when navigating the virtual space. To solve this problem, we use a single navigation metaphore in which different modes are organically integrated. In this paper we propose a helicopter pilot metaphor. Using the helicopter pilot metaphore means that the user navigates the virtual space like a pilot of a helicopter flying in space. In this paper, we distinguished six 2D movement spaces of the helicopter: (1) the movement on the horizontal plane, (2) the movement on the vertical plane,k (3) the pitch and yaw rotations about the current position, (4) the roll and pitch rotations about the current position, (5) the horizontal and vertical turning, and (6) the rotation about the target object. The six 3D movement spaces are visualized and displayed as a sequence of auxiliary windows. The user can select the desired movement space simply by jumping from one window to another. The user can select the desired movement by looking at the displaced 2D movement spaces. The movement of the camera in each movement space is controlled by the usual movements of the joystick.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.639-652
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• 2023

The fundamental background behind the introduction of the fishing vessel system is to allow petty small fishers to engage in pure fishery business activities with fishing vessels during normal times and engage in fishing vessel business only during specific periods (closed fishing season, etc.) thereby granting a qualification as an auxiliary tool for the economic activities of petty small fishers. In addition, fishing boats are allowed to engage in excursion ship activities using fishing vessels registered under the Fishing Vessels Act, the form of fishing vessels should also have a general and universal structure that is practically easy to engage in fishing activities in the field in accordance with the relevant regulations. However, most fishing vessel proprietors are currently focusing only on increasing income, and rather than building fishing vessels in a reasonable form suitable for the original purpose of general fishing vessels, they prefer an abnormal hull form equivalent to expediency, that is biased hull structure biased toward the fishing vessel business. As a result, it is causing serious problems in safety management as well as conflict [damaging relative equity in government support measures (tax-free oil supply, etc.), and depletion of livelihood-type fish stocks] with fishing vessel forces who consider the fishing vessel business only to be a part of the side job among all fishery business activities. Meanwhile, the most fundamental cause of this problem is that the current Fishing Management and Promotion Act, limits the concept of fishing vessels to fishing vessels registered under the Fishing Vessels Act, and applies survey standards accordingly. Accordingly, in this study, through analysis of the distribution status of fishing vessels, structural characteristics, operation status of fishing vessels, and the government's fishing promotion policies, etc., the relevant laws (regulations) have been reorganized to suit the current reality of the concept of fishing vessels to separate the current fishing vessel from fishing vessels and operate it as a fishing-only vessel.