• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.395-410
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• 2024

Text data is usually made up of a wide variety of unique words. Even in standard text data, it is common to find tens of thousands of different words. In text data analysis, usually, each unique word is treated as a variable. Thus, text data can be regarded as a dataset with a large number of variables. On the other hand, in text data classification, we often encounter class label imbalance problems. In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded. To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used. The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm. However, in datasets with a large number of variables, such as text data, errors may accumulate. This can potentially impact the performance of the kNN algorithm. In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data. Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.435-444
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• 2013

ATSC terrestrial DTV system can support high data rates for HDTV(High Definition Television) service, but it suffers from significant performance degradation caused by multipath fading. Thus, it is necessary to analyze multipath fading effects in order to enhance the DTV reception performance. Generally, DTV channel profile can be obtained by auto-correlation between reference pseudo random signal and received DTV signal. However, in the ATSC terrestrial DTV system, the estimation performance of DTV channel profile may be decreased due to the VSB modulation features. In this paper, improved DTV channel profile measurement technique is analyzed and proposed.

• Journal of KIISE:Information Networking
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• v.37 no.4
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• pp.284-293
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• 2010

For the successful deployment of Mobile Internet, fast handover technologies are essential. For the past few years several handover mechanisms are suggested, and Fast Handover for Proxy Mobile IPv6 (PFMIPv6) is one of the promising schemes for this purpose. In this paper, we propose a novel L2/L3 cross layer handover scheme based on ARIMA prediction model to apply PFMIPv6 to Mobile Internet environment effectively. Performance gains are evaluated in terms of probabilities of predictive-mode operation, handover latencies, packet loss probabilities, and signaling costs. Three mobilities models are used for our simulation: Manhattan Model, Open Area Model, and Freeway Model. Simulation results show that the proposed scheme can increase probabilities of predictive-mode operation and reduce handover latency, packet loss probabilities, and signaling cost.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2382-2390
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• 1999

In this paper, we employed a new quantization scheme called sample-adaptive product quantizer (SAPQ) to quantize image data based on the differential pulse code modulation (DPCM) coder, which has fixed length outputs and high bit rates. In order to improve the performance of traditional DPCM coders, the scalar quantizer should be replaced by the vector quantizer (VQ). As the bit rate increases, it will be nearly impossible to implement a conventional VQ or modified VQ, such as the tree-structured VQ, even if the modified VQ can significantly reduce the encoding complexity. SAPQ has a form of the feed-forward adaptive scalar quantizer having a short adaptation period. However, since SAPQ is a structurally constrained VQ, SAPQ can achieve VQ-level performance with a low encoding complexity. Since SAPQ has a scalar quantizer structure, by using the traditional scalar value predictors, we can easily apply SAPQ to DPCM coders. For synthetic data and real images, by employing SAPQ as the quantizer part of DPCM coders, we obtained a 2~3 dB improvement over the DPCM coders, which are based on the Lloyd-Max scalar quantizers, for data rates above 4 b/point.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1452-1459
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in and under Nyquist rate representation. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, conventional research works use a structural measurement matrix with which compressed sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are made to be improved at same time. However, the conventional researches did not compare the performances of the structural measurement matrix, affected by the block size. In this paper, by expanding a structural measurement matrix of conventional works, their performances are compared with different block sizes. Experimental results show that a structural measurement matrix with $4{\times}4$ Hadamard transform matrix provides superior performance in block size 4.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.364-374
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• 2012

The purpose of this study is to perform the reliability-corrected development cost estimation of the launch vehicle at the conceptual design phase. In order to estimate the launch vehicle development cost, the estimation method based on the independent variable such as the rocket performance and dry mass has been mainly implemented up to now. This approach has made the approximate cost estimation possible, however, the cost variation according to the reliability requirement could not be reflected. In this paper, the cost estimation methodology that introduces the reliability factor in addition to the performance and mass in the TRANSCOST model is presented in order to improve the limitation of current cost estimation method. The development cost of KSLV(Korea Space Launch Vehicle)-II is estimated on the basis of this newly implemented concept with reliability as an added parameter.

• The KIPS Transactions:PartA
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• v.13A no.2 s.99
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• pp.171-176
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• 2006

In this paper, we develop energy efficient designs for the Fast Fourier Transform (FFT) on FPGAs. Architectures for FFT on FPGAs are designed by investigating and applying techniques for minimizing the energy dissipation. Architectural parmeters such as degrees of vertical and horizontal parallelism are identified and a design choices. We determine design trade-offs using high-level performance estimation to obtain energy-efficient designs. We implemented a set storage types as parameters, on Xilinx Vertex-II FPGA to verify the estimates. Our designs dissipate 57% to 78% less energy than the optimized designs from the Xilinx library. In terms of a comprehensive metric such as EAT (Energy-Area-Time), out designs offer performance improvements of 3-13x over the Xilinx designs.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.157-167
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• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.