• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.49-54
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• 2013

Japan and South Korea since 2004 until now for the broadcast channel interference, by measuring the ongoing conflict are expected to prepare for the future, but Korea's preparation are not enough. In this study, it is pointed that cause of the interference through channel environmental analysis, and effective application of propagation prediction model was carried out between neighboring countries. Between Korea and Japan, radio duct occurs on hold due to changes in the refractive gradient, and comfirmed occurrence of broadcasts interference. The results are presented that 1% time variable, -91.80 [N-units/km], 10% time variable, -43.92 [N-units/km], 50% time variable, -586.19 [N-units/km], for effective refractive gradient. Proposed refractive gradient could contribute to actual radio propagation prediction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.117-123
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• 2018

In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.

• Journal of the Korean Magnetics Society
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• v.27 no.2
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• pp.63-69
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• 2017

Naval ships are particularly required to maintain acoustic and magnetic silence due to their operational characteristics. Among them, underwater magnetic field signals derived by ships are likely to be detected by threats such as surveillance systems and mine systems at close distance. In order to increase the survivability of the vessels, various techniques for reducing the magnetic field signal are being studied and it is necessary to consider not only the magnitude of the magnetic field signal but also the gradient of it. In this paper, we use the commercial electromagnetic finite element analysis tool to predict the induced magnetic field signal of ship's scaled model, and arrange the degaussing coil. And the optimum degaussing current of the coil was derived by applying the particle swarm optimization algorithm considering the gradient constraint. The validity of the optimal degaussing technique is verified analytically by comparing the magnetic field signals after the degaussing with or without gradient constraint.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.231-237
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• 2024

Brain injury causes persistent disability in survivors, and epidural hematoma(EDH) and subdural hematoma (SDH) resulting from cerebral hemorrhage can be considered one of the major clinical diseases. In this study, we attempted to automatically segment and hematomas due to cerebral hemorrhage in three dimensions based on computed tomography(CT) images. An improved GVF(gradient vector flow) algorithm was implemented for automatic segmentation of hematoma. After calculating and repeating the gradient vector from the image, automatic segmentation was performed and a 3D model was created using the segmentation coordinates. As a result of the experiment, accurate segmentation of the boundaries of the hematoma was successful. The results were found to be good even in border areas and thin hematoma areas, and the intensity, direction of spread, and area of the hematoma could be known in various directions through the 3D model. It is believed that the planar information and 3D model of the cerebral hemorrhage area developed in this study can be used as auxiliary diagnostic data for medical staff.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1047-1057
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• 2024

Recently, deep neural network-based reinforcement learning models have been applied in various advanced industrial fields such as autonomous driving, smart factories, and home networks, but it has been shown to be vulnerable to malicious adversarial attack. In this paper, we applied deep reinforcement learning models, DQN and PPO, to the autonomous driving simulation environment HighwayEnv and conducted three adversarial attacks: FGSM(Fast Gradient Sign Method), BIM(Basic Iterative Method), PGD(Projected Gradient Descent) and CW(Carlini and Wagner). In order to respond to adversarial attack, we proposed a method for deep learning models based on reinforcement learning to operate normally by removing noise from adversarial images using a bilateral filter algorithm. Furthermore, we analyzed performance of adversarial attacks using two popular metrics such as average of episode duration and the average of the reward obtained by the agent. In our experiments on a model that removes noise of adversarial images using a bilateral filter, we confirmed that the performance is maintained as good as when no adversarial attack was performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• Advances in nano research
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• v.6 no.1
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• pp.21-37
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• 2018

In the present article, wave dispersion behavior of a temperature-dependent functionally graded (FG) nanobeam undergoing rotation subjected to thermal loading is investigated according to nonlocal strain gradient theory, in which the stress numerates for both nonlocal stress field and the strain gradient stress field. The small size effects are taken into account by using the nonlocal strain gradient theory which contains two scale parameters. Mori-Tanaka distribution model is considered to express the gradually variation of material properties across the thickness. The governing equations are derived as a function of axial force due to centrifugal stiffening and displacements by applying Hamilton's principle according to Euler-Bernoulli beam theory. By applying an analytical solution, the dispersion relations of rotating FG nanobeam are obtained by solving an eigenvalue problem. Obviously, numerical results indicate that various parameters such as angular velocity, gradient index, temperature change, wave number and nonlocality parameter have significant influences on the wave characteristics of rotating FG nanobeams. Hence, the results of this research can provide useful information for the next generation studies and accurate deigns of nanomachines including nanoscale molecular bearings and nanogears, etc.

• Journal of the Korean Society of Visualization
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• v.8 no.3
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• pp.35-40
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• 2010

We have investigated different radial temperature gradient effect on the stability of Taylor-Couette flow. The radius ratio and aspect ratio of the model was 0.825 and 48, respectively. Two heating exchangers were used for generating different temperature gradient along the radial direction. The change of flow regime in the Taylor-Couette flow was studied by increasing the Reynolds number. The results showed that: as Gr is increased in helical vortex flow regime, the vortices with the same direction of convection flow increased in size, and the vortex moving velocity also increased. It is also shown that the presence of temperature gradient obviously increased the flow instability when the Richardson number is larger than 0.0045.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.9
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• pp.1073-1084
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• 1999

The effects of secondary flow on the structure of a turbulent wake generated by a flat plate was investigated experimentally. The secondary flow was induced In a $90^{\circ}$ curved duct in which the flat plate wake generator was installed. The wake generator was installed in such a way that the wake velocity gradient exists in the span wise direction of the curved duct. Measurements were made in the plane containing the mean radius of curvature where pressure gradient and curvature effects were small compared with the secondary flow effect. All six components of the Reynolds stresses were measured in the curved duct. Turbulence intensities in the curved wake are higher than those in the straight wake due to an increase of the turbulent kinetic energy production by the secondary flow. In the inner wake region, shear stress and strain in the plane containing the velocity gradient of the wake show opposite signs with respect to each other, so that eddy viscosity Is negative in this region. This indicates that gradient-diffusion type turbulence models are not appropriate to simulate this type of flow.