• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.256-268
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• 2008

This paper proposes an efficient scheme to track the time variant channel induced by multi-path Rayleigh fading in mobile wireless Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems with null sub-carriers. In the proposed method, a blind channel response predictor is designed to cope with the time variant channel. The proposed channel tracking scheme consists of a frequency domain estimation approach that is coupled with a Minimum Mean Square Error (MMSE) time domain estimation method, and does not require any matrix inverse calculation during each OFDM symbol. The main attributes of the proposed scheme are its reduced computational complexity and good tracking performance of channel variations. The simulation results show that the proposed method exhibits superior performance than the conventional channel tracking method [4] in time varying channel environments. At a Doppler frequency of 100Hz and bit error rates (BER) of 10-4, signal-to-noise power ratio (Eb/N0) gains of about 2.5dB are achieved relative to the conventional channel tracking method [4]. At a Doppler frequency of 200Hz, the performance difference between the proposed method and conventional one becomes much larger.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Journal of the Korean GEO-environmental Society
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• v.24 no.9
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• pp.41-46
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• 2023

Numerical analysis of frost heave is challenging due to the influence of soil and environmental factors. Thermo-hydromechanical coupled analysis relies heavily on excessive input variables and primarily focuses on validating clayey soils, so it is limited to frost susceptible silty soils. An empirical approach based on thermodynamics offers relatively simple frost heave analysis and the advantage of linking constitutive equations with frost heave to enable geomechanical interpretations. In this paper, we introduce an empirical numerical model using the Segregation Potential (SP) and evaluate reliability through comparative analysis with experimental results of frost susceptible silty soils. While the SP model enables frost heave analysis for the given silty soils, further investigation on various silty soils is necessary to gather data on key input variables.

• Earthquakes and Structures
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• v.26 no.2
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• pp.149-162
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• 2024

Aiming at the current research on the dynamic response analysis of the vehicle-bridge system under earthquake, which fails to comprehensively consider the impact of seismic wave incidence angles, terrain effects and soil-structure dynamic interaction on the bridge structure, this paper proposes a multi-point excitation input method that can consider the oblique incidence seismic P Waves based on the viscous-spring artificial boundary theory, and verifies the accuracy and feasibility of the input method. An overall numerical model of vehicle-bridge-soil foundation system in valley terrain during oblique incidence of seismic P-wave is established, and the effects of seismic wave incidence characteristics, terrain effects, soil-structure dynamic interactions, and vehicle speeds on the dynamic response of the bridge are analyzed. The research results indicate that with an increase in P wave incident angle, the vertical dynamic response of the bridge structure decreased while the horizontal dynamic response increased significantly. Traditional design methods which neglect multi-point excitation would lead to an unsafe structure. The dynamic response of the bridge structure significantly increases at the ridge while weakening at the valley. The dynamic response of bridge structures under earthquake action does not always increase with increasing train speed, but reaches a maximum value at a certain speed. Ignoring soil-structure dynamic interaction would reduce the vertical dynamic response of the bridge piers. The research results can provide a theoretical basis for the seismic design of vehicle-bridge systems in complex mountainous terrain under earthquake excitation.

• Journal of the Korean GEO-environmental Society
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• v.25 no.7
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• pp.29-34
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• 2024

Frost heave is one of the significant engineering characteristics of frozen ground and causes severe damages on geo-structures. Although thermo-hydro coupled analyses have been developed to predict frost heave behavior, these analyses involve excessive input parameters and have primarily been validated for frost heave in clayey soils. Frost heave mainly occurs in silty soils, which have relatively higher permeability compared to clayey soils, necessitating careful attention. This study introduces empirical models and verifies their reliability for silty soils. By using the validated model, the correlation of key input parameters is derived, which is expected to enhance the applicability of thermal-mechanical analysis for geo-structures on frozen ground in the future.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.271-271
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• 2012

Many studies have been investigated on high density plasma source (Electron Cyclotron Resonance, Inductively Coupled Plasma, Helicon plasma) for large area source after It is announced that productivity of plasma process depends on plasma density. In this presentation, we will propose the new concept of the multiple source, which consists of a parallel connection of ICP sources and helicon plasma sources. For plasma uniformity, equivalent power (especially, equivalent current in ICP & Helicon) should distribute on each source. We design power feeding line as coaxial transmission line with same length of ground line in each source for equivalent power distribution. And we confirm the equivalent power distribution with simulation and experimental result. Based on basic study, we develop the plasma source for oxidation in semiconductor process. we will discuss the relationship between the processing parameters (With or WithOut magnet, operating pressure, input power ). In ICP, plasma density uniformity is uniform. In ICP with magnet (or Helicon) plasma density is not uniform. As a result, new design (magnet arrangement and gas distributor and etc..) are needed for uniform plasma density in ICP with magnet and Helicon.

• Coupled systems mechanics
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• v.7 no.5
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• pp.509-524
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• 2018

A hierarchical multi-scale modeling strategy devoted to the study of a Bitumen-Bound Gravel (BBG) is presented in this paper. More precisely, the paper investigates the temperature-dependent linear viscoelastic of the material when submitted to low deformations levels and moderate number of cycles. In such a hierarchical approach, 3D digital Representative Elementary Volumes are built and the outcomes at a scale (here, the sub-mesoscale) are used as input data at the next higher scale (here, the mesoscale). The viscoelastic behavior of the bituminous phases at each scale is taken into account by means of a generalized Maxwell model: the bulk part of the behavior is separated from the deviatoric one and bulk and shear moduli are expanded into Prony series. Furthermore, the viscoelastic phases are considered to be thermorheologically simple: time and temperature are not independent. This behavior is reproduced by the Williams-Landel-Ferry law. By means of the FE simulations of stress relaxation tests, the parameters of the various features of this temperature-dependent viscoelastic behavior are identified.

• Advances in nano research
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• v.8 no.3
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• pp.245-254
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• 2020

This work examines the fundamental vibrational characteristics of a spinning CNT-based nano-rotor assuming a nonlocal elasticity Euler-Bernoulli beam theory. The rotary inertia, gyroscopic, and rotor mass unbalance effects are all taken into consideration in the beam model. Assuming a nonlocal theory, two coupled 6th-order partial differential equations governing the vibration of the rotating SWCNT are first derived. In order to acquire the natural frequencies and dynamic response of the nano-rotor system, the nonlinear equations of motion are numerically solved. The nano-rotor system frequency spectrum is shown to exhibit two distinct frequencies: one positive and one negative. The positive frequency is known as to represent the forward whirling mode, whereas the negative characterizes the backward mode. First, the results obtained within the framework of this numerical study are compared with few existing data (i.e., molecular dynamics) and showed an overall acceptable agreement. Then, a thorough and detailed parametric study is carried out to study the effect of several parameters on the nano-rotor frequencies such as: the nanotube radius, the input angular velocity and the small scale parameters. It is shown that the vibration characteristics of a spinning SWCNT are significantly influenced when these parameters are changed.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.