• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.7
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• pp.692-698
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• 2003

This paper presents IM model which is produced in 3 port RF passive component. We calculated forward and reflect PIM of 3 port component by using the model. And they are reasonable agreement with measurement result like below. In the case of forward IM, calculated result is -122 dBc(at 43 dBm), which differ 2.5 dB with measurement result. And reflected IM, the average calculated result is -130 dBc(at 43 dBm), which differ 3 dB with measurement result. The results which are calculated and measured under various condition(IM source level, frequency sweep, contour length, etc.) show that forward method gets more fixed result than that of reflect. Consequently, PIM of 3 port component is predicted and analyzed for desensitizing by the proposed model.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2005.11a
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• pp.143-148
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• 2005

The objective of this study is to estimate RF signal strength over sea and land surfaces. For this work we calculated scattering by land with DEM(Digital Elevation Model) and sea surface with RMS surface height. and we selected two area inland and sea shore as RX point. And for each area, we get VV-pol and HH-pol characteristic of scattering at 2.2GHz.

• Advances in materials Research
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• v.1 no.1
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• pp.37-49
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• 2012

In this paper, we present modeling and characterization of CNT-based TSVs to be used in high-frequency RF applications. We have developed an integrated model of CNT-based TSVs by incorporating the quantum confinement effects of CNTs with the kinetic inductance phenomenon at high frequencies. Substrate parasitics have been appropriately modeled as a monolithic microwave capacitor with the resonant line technique using a two-polynomial equation. Different parametric variations in the model have been outlined as case studies. Furthermore, electrical performance and signal integrity analysis on different cases have been used to determine the optimized configuration for CNT-based TSVs for high frequency RF applications.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.561-589
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• 2022

When multiple classifications and regression trees are combined, tree-based ensemble models, such as random forest (RF) and Bayesian additive regression trees (BART), are produced. We compare the model structures and performances of various ensemble models for regression settings in this study. RF learns bootstrapped samples and selects a splitting variable from predictors gathered at each node. The BART model is specified as the sum of trees and is calculated using the Bayesian backfitting algorithm. Throughout the extensive simulation studies, the strengths and drawbacks of the two methods in the presence of missing data, high-dimensional data, or highly correlated data are investigated. In the presence of missing data, BART performs well in general, whereas RF provides adequate coverage. The BART outperforms in high dimensional, highly correlated data. However, in all of the scenarios considered, the RF has a shorter computation time. The performance of the two methods is also compared using two real data sets that represent the aforementioned situations, and the same conclusion is reached.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1861-1868
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• 2008

In this paper, Passive Telemerty RF Sensor System using Unscented Kalman Filter algorithm(UKF) is proposed. General Passive Telemerty RF Sensor System means that it should be "wireless", "implantable" and "batterless". Conventional Passive Telemerty RF Sensor System adopts Integrated Circuit type, but there are defects like complexity of structure and limit of large power consumption in some cases. In order to overcome these kinds of faults, Passive Telemetry RF Sensor System based on inductive coupling principle is proposed in this paper. Because passive components R, L, C have stray parameters in the range of high frequency such as about 200[KHz] used in this paper, Passive Telemetry RF Sensor System considering stray parameters has to be derived for accurate model identification. Proposed Passive Telemetry RF Sensor System is simple because it consists of R, L and C and measures the change of environment like pressure and humidity in the type of capacitive value. This system adopted UKF algorithm for estimation of this capacitive parameter included in nonlinear system like Passive Telemetry RF Sensor System. For the purpose of obtaining learning data pairs for UKF Algorithm, Phase Difference Detector and Amplitude Detector are proposed respectively which make it possible to get amplitude and phase between input and output voltage. Finally, it is verified that capacitive parameter of proposed Passive Telemetry RF Sensor System using UKF algorithm can be estimated in noisy environment efficiently.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.10
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• pp.60-66
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• 1998

The hot carrier induced performance degradation of 0.8${\mu}{\textrm}{m}$ RF NMOSFET has been investigated within the general framework of the degradation mechanism. The device degradation model of an unit finger gate MOSFET could be applied for the device degradation of the multi finger gate RF NMOSFET. The reduction of cut-off frequency and maximum frequency can be explained by the transconductance reduction and the drain output conductance increase, which are due to the interface state generation after the hot carrier stressing. From the correlation between hot carrier induced DC and RF performance degradation, we can predict the RF performance degradation just by the DC performance degradation measurement.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

• Journal of the Korean Society of Radiology
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• v.11 no.5
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• pp.361-370
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• 2017

In the Brain MRI, RF Pulse is irradiated on the human body in order to acquire an image. At this time, a considerable part of the irradiated RF Pulse energy is absorbed as it is in our body. This will raise the temperature of the human body, but depending on the extent of exposure, it will affect the human body. The change of the SAR and the temperature of the head according to the change of the magnetic field strength is examined. And to investigate the difference in results depending on the use of dental implant. In the human head model, 64 MHz RF Pulse frequency generated from 1.5 T, 128 MHz RF Pulse frequency generated from 3.0 T, and 298 MHz RF Pulse frequency generated from 7.0 T send a frequency and experiment was performed using dental implant using the XFDTD program, we measured the SAR and body temperature changes around the head. The SAR value showed up to about 5800 times the difference at the RF Pulse frequency of 256 MHz, when with dental implant than without dental implant and as the frequency increased, the use of the dental implant increased difference in the SAR value. The change of the temperature of the head showed a temperature rise nearly 2 to 4 times when with dental implant than without dental implant. As the RF Pulse frequency increase, the SAR value increase, but the change of the temperature of the head decrease. Because of as the frequency increase, wavelength is smaller and the more the amount absorbed by the surface of the human. Physiological and biochemical studies of the human body ar necessary through studies of the presence of dental implant and the cause of reaction caused by change in the RF Pulse frequency.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.