• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.29-34
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• 2023

A clear and efficient design method for a microstrip-to-waveguide inline transition, which is based on an analytical model, is presented. The transition consists of three parts: a microstrip-to-SIW transition, a dielectric-loaded waveguide with substrate-height, and a stepped-height waveguide. The shape of the transitional structure is formed for impedance matching. Two equivalent type0s of dielectric-loaded transitional structures are proposed. The design method is applicable to any size of the waveguide, but a design method of two Ka-band transitions is demonstrated. The proposed transitions, in a back-to-back configuration, have less than 1.2 dB insertion loss and more than 15 dB return loss from 29.8 GHz to 38.2 GHz.

• Journal of the Korean Geotechnical Society
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• v.39 no.6
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• pp.5-12
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• 2023

Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.3-20
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• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.2
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• pp.9-17
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• 2010

Recently, there has been much interest in orthogonal frequency division multiplexing (OFDM) for next generation wireless wideband communication systems. OFDM is a special case of multicarrier transmission, where a single data stream is transmitted over a number of lower-rate subcarriers. One of the main reasons to use OFDM is to increase robustness against frequency-selective fading or narrowband interference. However, in the radio systems it is also important to distortion introduced by high power amplifiers (HPA's) such as solid state power amplifier (SSPA) considered in this paper. Since the signal amplitude of the OFDM system is Rayleigh-distributed, the performance of the OFDM system is significantly degraded by the nonlinearity of the HPA in the OFDM transmitter. In this paper, we propose a canonical piecewise-linear (PWL) model based digital predistorter to prevent signal distortion and spectral re-growth due to the high peak-to-average power ratio (PAPR) of OFDM signal and the nonlinearity of HPA's. Computer simulation on an OFDM system under additive white Gaussian noise (AWGN) channels with QPSK, 16-QAM and 64-QAM modulation schemes and modulator/demodulator implemented with 1024-point FFT/IFFT, demonstrate that the proposed predistorter achieves significant performance improvement by effectively compensating for the nonlinearity introduced by the SSPA.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.221-221
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• 2014

The present work proposes an improved numerical simulator for design and modification of large area capacitively coupled plasma (CCP) processing chamber. CCP, as notoriously well-known, demands the tremendously huge computational cost for carrying out transient analyses in realistic multi-dimensional models, because electron dissociations take place in a much smaller time scale (${\Delta}t{\approx}10-8{\sim}10-10$) than time scale of those happened between neutrals (${\Delta}t{\approx}10-1{\sim}10-3$), due to the rf drive frequencies of external electric field. And also, for spatial discretization of electron flux (Je), exponential scheme such as Scharfetter-Gummel method needs to be used in order to alleviate the numerical stiffness and resolve exponential change of spatial distribution of electron temperature (Te) and electron number density (Ne) in the vicinity of electrodes. Due to such computational intractability, it is prohibited to simulate CCP deposition in a three-dimension within acceptable calculation runtimes (<24 h). Under the situation where process conditions require thickness non-uniformity below 5%, however, detailed flow features of reactive gases induced from three-dimensional geometric effects such as gas distribution through the perforated plates (showerhead) should be considered. Without considering plasma chemistry, we therefore simulated flow, temperature and species fields in three-dimensional geometry first, and then, based on that data, boundary conditions of two-dimensional plasma discharge model are set. In the particular case of SiH4-NH3-N2-He CCP discharge to produce deposition of SiNxHy thin film, a cylindrical showerhead electrode reactor was studied by numerical modeling of mass, momentum and energy transports for charged particles in an axi-symmetric geometry. By solving transport equations of electron and radicals simultaneously, we observed that the way how source gases are consumed in the non-isothermal flow field and such consequences on active species production were outlined as playing the leading parts in the processes. As an example of application of the model for the prediction of the deposited thickness uniformity in a 300 mm wafer plasma processing chamber, the results were compared with the experimentally measured deposition profiles along the radius of the wafer varying inter-electrode gap. The simulation results were in good agreement with experimental data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.6
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• pp.553-558
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• 2011

This paper describes the development and test result of S-band Transmitter flight model(FM) of STSAT-3 by satellite research center(SaTReC), KAIST. The communication sub-system of STSAT-3 is consist of two different frequency band channels, S-band for Telemetry & Command and X-band for mission data. S-band Transmitter(STX) functionally made of modulator, frequency synthesizer, power amp and DC/DC converter. The transmission data is modulated by FSK(Frequency Shift Keying) and the interface between spacecraft sub-module and STX is RS-422 standard method. The FM STX is based on modular design. The RF output power of STX is 1.5W(31.7dBm) and BER of STX is under $1{\times}10^{-5}$ which meets the specification respectively. The FM STX is delivered Spacecraft Assembly, Integration and Test(AIT) level through the completion of functional Test and environmental(vibration, thermal vacuum) Test successfully.