• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.995-1002
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• 2020

This paper investigates the achievable data rate for non-orthogonal multiple access(NOMA) with correlated information sources(CIS), under the binary phase shift keying(BPSK) modulation, in contrast to most of the existing NOMA designs using continuous Gaussian input modulations. First, the closed-form expression for the achievable data rate of NOMA with CIS and BPSK is derived, for both users. Then it is shown by numerical results that for the stronger channel user, the achievable data rate of CIS reduces, compared with that of independent information sources( IIS). We also demonstrate that for the weaker channel user, the achievable data rate of CIS increases, compared with that of IIS. In addition, the intensive analyses of the probability density function(PDF) of the observation and the inter-user interferennce(IUI) are provided to verify our theoretical results.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.74-80
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• 2021

Korea Atomic Energy Research Institute (KAERI) has been operating an integral effects test facility, the Advanced Thermal-Hydraulic Test Loop for Accident Simulation (ATLAS), according to APR1400 for transient experimental and design basis accident simulation. Moreover, based on the experimental data, the domestic standard problem (DSP) program has been conducted in Korea to validate system codes. Recently, through DSP-05, the performance of the passive auxiliary feedwater system (PAFS) in the event of multiple steam generator tube rupture (MSGTR) has been analyzed. However, some errors exist in the reference input model distributed for DSP-05. Furthermore, the calculation results of the heat loss correlation for the secondary system presented in the technical report of the reference indicate that a large difference is present in heat loss from the target value. Thus, in this study, the reference model is corrected using the geometric information from the design report and drawings of ATLAS. Additionally, a new heat loss correlation is suggested by fitting the results of the heat loss tests. Herein, MSGTR-PAFS accident analysis is performed using MARS-KS 1.5 with the improved model. The steady-state calculation results do not significantly differ from the experimental values, and the overall physical behavior of the transient state is properly predicted. Particularly, the predicted operating time of PAFS is similar to the experimental results obtained by the modified model. Furthermore, the operating time of PAFS varies according to the heat loss of the secondary system, and the sensitivity analysis results for the heat loss of the secondary system are presented.

• Convergence Security Journal
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• v.20 no.4
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• pp.3-12
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• 2020

Although the related regulations or guidelines are not specified in the defense weapon system R&D process, R&D authorities frequently encounter problems that require rational decision-making. If the rational process is not applied in the matter of alternative choice, the project could be disrupted, which can result in longer project periods or more resource provision. In particular, a variety of decision-making methods are needed for test&evaluation of missile R&D. The issue of selecting a test site is one of the representative decision-making problems. If it is needed to determine the priority of multiple sites, Delphi Method and Analytic Hierarchy Process(AHP) will be applied. However, if the input of cost is to be considered, Data Envelopment Analysis(DEA) is more valuable to solve the problem. This paper proposes a solution to handle quantitatively various decision-making problems that can occur in missile flight test, and shows how DEA is applied through a simulated case study of selecting a foreign test site.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.59-69
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• 2021

Historical records of earthquakes are generally used as a basis to extrapolate the instrumental earthquake catalog in time and space during the probabilistic seismic hazard analysis (PSHA). However, the historical catalogs' input parameters determined through historical descriptions rather than any quantitative measurements are accompanied by considerable uncertainty in PSHA. Therefore, quantitative assessment to verify the historical earthquake parameters is essential for refining the reliability of PSHA. This study presents an approach and its application to constrain reliable ranges of the magnitude and corresponding epicenter of historical earthquakes. First, ranges rather than specific values of ground motion intensities are estimated at multiple locations with distances between each other for selected historical earthquakes by reviewing observed co-seismic natural phenomena, structural damage levels, or felt areas described in their historical records. Based on specific objective criteria, this study selects only one earthquake (July 24, 1643), which is potentially one of the largest historical earthquakes. Then, ground motion simulations are performed for sufficiently broadly distributed epicenters, with a regular grid to prevent one from relying on strong assumptions. Calculated peak ground accelerations and velocities in areas with the historical descriptions on corresponding earthquakes are converted to intensities with an empirical ground motion-intensity conversion equation to compare them with historical descriptions. For the ground motion simulation, ground motion prediction equations and a frequency-wavenumber method are used to consider the effects of possible source mechanisms and stress drop. From these quantitative calculations, reliable ranges of epicenters and magnitudes and the trade-off between them are inferred for the earthquake that can conservatively match the upper and lower boundaries of intensity values from historical descriptions.

• Journal of Soil and Groundwater Environment
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• v.27 no.spc
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• pp.92-98
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• 2022

Although various methods have been proposed to assess groundwater vulnerability, most of the models merely consider the mobility of contaminants (i.e., intrinsic vulnerability), and the attenuation capacity of vadose zone is often neglected. This study proposed an evaluation model for the attenuation capacity of vadose zone to supplement the limitations of the existing index method models for assessing groundwater vulnerability. The evaluation equation for quantifying the attenuation capacity was developed from the combined linear regression and weighted scaling methods based on the lab-scale experiments using various vadose zone soils having different physical and biogeochemical properties. The proposed semi-quantifying model is expected to effectively assess the attenuation capacity of vadose zone by identifying the main influencing factors as input parameters together with proper weights derived from the coefficients of the regression results. The subsequent scoring and grading system has great versatility while securing the objectivity by effectively incorporating the experimental results.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.514-523
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• 2022

Concentrations of perfluoroalkyl substances (PFASs) were measured in seawater, sediment, and biota collected from Gunsan coast, Korea to investigate their occurrence, distribution, and risk of exposure to humans through seafood consumption. The total concentrations of PFASs in seawater, sediment, and biota ranged from 5.97 to 74.9 ng/L, 0.01 to 13.3 ng/g dry weight, and 0.02 to 5.73 ng/g wet weight, respectively. Predominant PFAS compounds differed across matrices, indicating that the distribution of PFASs in multiple environmental samples is governed by their carbon-chain length. The concentrations of PFASs in seawater were significantly negatively correlated with salinity (P<0.01), suggesting terrestrial input (including land-used PFASs) as the major source of PFAS contamination in coastal environments. The estimated daily intakes (EDIs) of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) through seafood consumption were 0.05 and 0.06 ng/kg body weight/day, respectively. The EDIs of PFOA and PFOS measured in this study were lower those the proposed by the United States Environmental Protection Agency and Canada guidelines, indicating limited health risk for Korean population from PFASs through consumption of seafood from Gunsan coastal environment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.371-380
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• 2022

Convolutional neural networks are widely used to manipulate data arranged in a grid, such as images. A general convolutional neural network consists of a convolutional layers and a fully connected layers, and each layer contains a nonlinear activation functions. This paper proposes a combined parametric activation function to improve the performance of convolutional neural networks. The combined parametric activation function is created by adding the parametric activation functions to which parameters that convert the scale and location of the activation function are applied. Various nonlinear intervals can be created according to parameters that convert multiple scales and locations, and parameters can be learned in the direction of minimizing the loss function calculated by the given input data. As a result of testing the performance of the convolutional neural network using the combined parametric activation function on the MNIST, Fashion MNIST, CIFAR10 and CIFAR100 classification problems, it was confirmed that it had better performance than other activation functions.