• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.4
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• pp.427-438
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• 1998

The bit error rate(BER) performance of Trellis Coded QPSK signal in the presence of cochannel interference (CCI) and Rician fading is investigated. The trellis coded QPSK system adopts Maximum Ratio Combining (MRC) diversity reception and Reed-Solomon code to enhance system performance. Using the derived error probability equation, the error performance of trellis coded QPSK system has been evaluated and shown in figures to discuss as a function of signal power to noise power ratio (SNR), signal power to interference power ratio(SIR), direct to indirect signal power ratio ($K_R$), the number of diversity branch (M), the frame length of Reed-Solomon code (n), the number of error correction symbol (t), and the number of state of trellis encoder. From the results, we know that proposed system is affected by cochannel interference and fading in microcell environment. Also, BER performance of Trellis Coded QPSK system can be improved as increasing either the power of desired signal or the value of SIR. And the BER floor in microcellular system is caused by the cochannel interference and it occurs at high BER when SIR is low. And Reed-Solomon code (n=15, t=2) is more effective to restrain the affection of CCI and fading than MRC diversity reception (M=2).

• Korean Chemical Engineering Research
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• v.46 no.4
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• pp.739-746
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• 2008

The aim of this study is finding the optimal design parameters and the optimal operation variables of a reactive distillation column. Different from steady state optimization, dynamic optimization makes it possible considering operation ability as well as design problems at process design step. For performing dynamic optimization, dynamic simulation should be done first. If dynamic simulation is already finished, dynamic optimization can be performed with less effort than that of dynamic simulation.Reactive distillation systems involving reaction and separation in a single unit have the potential to reduce capital and operating costs, particularly when reaction have conversion constraint or when azeotropes exist making conventional separation difficult and expensive. This study here present work on the continuous distillation process, the homogeneous catalyzed esterification of methanol and acetic acid, the synthesis of methyl acetate. Based on an equilibrium stage model of a reactive distillation column a dynamic optimization problem was formulated and solved. And the results were verified by performing dynamic simulation and showing the variation of conversion and purity as the variation of the operation variables. As the results of dynamic optimization, this study found optimal feed ratio, reflux ratio and reboiler duty of this system. And as this study applied it to dynamic simulations the dynamic characteristics of a reactive distillation column are showed under optimal operating condition.

• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.315-322
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• 2010

The phase-screen and the split-step Fourier migrations, which are implemented in both the frequency-wavenumber and frequency-space domains by using one-way scalar wave equation, allow imaging in laterally heterogeneous media with less computing time and efficiency. The generalized-screen migration employs the series expansion of the exponential, unlike the phase-screen and the split-step Fourier migrations which assume the vertical propagation in frequency-wavenumber domain. In addition, since the generalized-screen migration generalizes the series expansion of the vertical slowness, it can utilize higher-order terms of that series expansion. As a result, the generalized-screen migration has higher accuracy in computing the propagation with wide angles than the phase-screen and split-step Fourier migrations for media with large and rapid lateral velocity variations. In this study, we developed a 2D prestack generalized-screen migration module for imaging a complex subsurface efficiently, which includes various dips and large lateral variations. We compared the generalized-screen propagator with the phase-screen propagator for a constant perturbation model and the SEG/EAGE salt dome model. The generalized-screen propagator was more accurate than the phase-screen propagator in computing the propagation with wide angles. Furthermore, the more the higher-order terms were added for the generalized-screen propagator, the more the accuracy was increased. Finally, we compared the results of the generalizedscreen migration with those of the phase-screen migration for a model which included various dips and large lateral velocity variations and the synthetic data of the SEG/EAGE salt dome model. In the generalized-screen migration section, reflectors were positioned more accurately than in the phase-screen migration section.

• Korean Journal of Optics and Photonics
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• v.31 no.2
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• pp.59-70
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• 2020

We discuss a modified numerical model based on the Monte Carlo radiative transfer (MCRT) method, i.e., the MCRT matrix method, for the analysis of atmospheric scattering effects in three-dimensional flash LIDAR systems. Based on the MCRT method, the radiative transfer function for a LIDAR signal is constructed in a form of a matrix, which corresponds to the characteristic response. Exploiting the superposition and convolution of the characteristic response matrices under the paraxial approximation, an extended computer simulation model of an overall flash LIDAR system is developed. The MCRT matrix method substantially reduces the number of tracking signals, which may grow excessively in the case of conventional Monte Carlo methods. Consequently, it can readily yield fast acquisition of the signal response under various scattering conditions and LIDAR-system configurations. Using the computational model based on the MCRT matrix method, we carry out numerical simulations of a three-dimensional flash LIDAR system operating under different atmospheric conditions, varying the scattering coefficient in terms of visible distance. We numerically analyze various phenomena caused by scattering effects in this system, such as degradation of the signal-to-noise ratio, glitches, and spatiotemporal spread and time delay of the LIDAR signals. The MCRT matrix method is expected to be very effective in analyzing a variety of LIDAR systems, including flash LIDAR systems for autonomous driving.

• Polymer(Korea)
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• v.33 no.4
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• pp.297-306
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• 2009

A homologous series of side chain liquid crystalline polymers, poly [1-{4-(4'-cyanophenylazo)phenoxyalkyloxy}ethylene]s(CAPETn, where n, the number of methylene units in the spacer, is $2{\sim}10$) were synthesized from poly(vinyl alcohol) and 1-{4-(4'-cyanophenylazo)phenoxy}alkylbromides(CAPBn, n=$2{\sim}10$), and their thermotropic liquid crystalline phase behaviors were investigated. The CAPBn with n of $2{\sim}5$ did not show any liquid crystalline behavior, while those with n of 6 and $7{\sim}10$ showed enantiotropic and monotropic nematic phases, respectively. In contrast, among the CAPETn polymers, only CAPET5 exhibited an enantiotropic nematic phase, while other polymers showed monotropic nematic phases. The isotropic-nematic transition temperatures of CAPETns and their entropy variation at the phase transition that were higher values than those of CAPBns, demonstrated a typical odd-even effect as a function of n. These phase transition behaviors were disscussed in terms of the 'virtual trimer model' by Imrie. The mesophase properties of CAPETns were largely different from those reported for the polymers in which the (cyanophenylazo) phenoxy groups are attached to polyacrylate, polymethacrylate, and polystyrene backbones through polymethylene spacers. The results indicate that the mode of chemical linkage of the side group with the main chain plays an important role in the formation, stabilization, and type of mesophase.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.161-172
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• 2003

This study has evaluated the stream gauge network with the main emphasis on if the current stream gauge network can catch the runoff characteristics of the basin. As the evaluation of the stream gauge network in this study does not consider a special purpose of a stream gauge, nor the effect from a hydraulic structure, it becomes an optimization of current stream gauge network under the condition that each stream gauge measures the natural runoff volume. This study has been applied to the Nam-Han River Basin for the optimization of total 31 stream gauge stations using the entropy concept. Summarizing the results are as follows. (1) The unit hydrograph representing the basin response from rainfall can be transferred into a probability density function for the application of the entropy concept to optimize the stream gauge network. (2) Accurate derivation of unit hydrographs representing stream gauge sites was found the most important part for the evaluation of stream gauge network, which was assured in this research by comparing the measured and derived unit hydrographs. (3) The Nam-Han River Basin was found to need at least 28 stream gauge stations, which was derived by considering both the shape of the unit hydrograph and the runoff volume. If considering only the shape of the unit hydrograph, the number of stream gauges required decreases to 23.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.229-239
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• 2018

The purpose of this study was to effectively produce the biosugar from cell wall of lipid extracted microalgae (LEA) by using microwave-assisted pretreatment without enzymatic hydrolysis process. Response surface methodology (RSM) was applied to optimization of microwave-assisted pretreatment conditions for the production of biosugar based on enzyme-free process from LEA. Microwave power (198~702 W), extraction time (39~241 sec), and sulfuric acid (0~1.0 mol) were used as independent variables for central composite design (CCD) in order to predict optimum pretreatment conditions. It was noted that the pretreatment variables that affect the production of glucose (C6) and xylose (C5) significantly have been identified as the microwave power and extraction time. Additionally, the increase in microwave power and time had led to an increase in biosugar production. The superimposed contour plot for maximizing dependent variables showed the maximum C6 (hexose) and C5 (pentose) yields of 92.7 and 74.5% were estimated by the predicted model under pretreatment condition of 700 w, 185.7 sec, and 0.48 mol, and the yields of C6 and C5 were confirmed as 94.2 and 71.8% by experimental validation, respectively. This study showed that microwave-assisted pretreatment under low temperature below $100^{\circ}C$ with short pretreatment time was verified to be an effective enzyme free pretreatment process for the production of biosugar from LEA compared to conventional pretreatment methods.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.