• Journal of KIISE
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• v.42 no.5
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• pp.629-636
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• 2015

The types of errors corrected by a Korean spelling and grammar checker can be classified into isolated-term spelling errors and context-sensitive spelling errors (CSSE). CSSEs are difficult to detect and to correct, since they are correct words when examined alone. Thus, they can be corrected only by considering the semantic and syntactic relations to their context. CSSEs, which are frequently made even by expert wiriters, significantly affect the reliability of spelling and grammar checkers. An existing Korean spelling and grammar checker developed by P University (KSGC 4.5) adopts hand-made correction rules for correcting CSSEs. The KSGC 4.5 is designed to obtain very high precision, which results in an extremely low recall. Our overall goal of previous works was to improve the recall without considerably lowering the precision, by generalizing CSSE correction rules that mainly depend on linguistic knowledge. A variety of rule-based methods has been proposed in previous works, and the best performance showed 95.19% of average precision and 37.56% of recall. This study thus proposes a statistics based method using a conditional probability model with dynamic window sizes. in order to further improve the recall. The proposed method obtained 97.23% of average precision and 50.50% of recall.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.2-43
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• 2021

We address a question of what are three main channels that can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations among the nine channels of the Atmospheric Imaging Assembly on the Solar Dynamics Observatory using a deep learning model based on conditional generative adversarial networks. In this study, we develop 170 deep learning models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly they are representative coronal, photospheric, and chromospheric lines, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.131-138
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• 2023

This paper is related to performance improvement of Image-to-Image translation using Relativistic Average Patch GAN and Residual in Residual Dense Block. The purpose of this paper is to improve performance through technical improvements in three aspects to compensate for the shortcomings of the previous pix2pix, a type of Image-to-Image translation. First, unlike the previous pix2pix constructor, it enables deeper learning by using Residual in Residual Block in the part of encoding the input image. Second, since we use a loss function based on Relativistic Average Patch GAN to predict how real the original image is compared to the generated image, both of these images affect adversarial generative learning. Finally, the generator is pre-trained to prevent the discriminator from being learned prematurely. According to the proposed method, it was possible to generate images superior to the previous pix2pix by more than 13% on average at the aspect of FID.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.367-379
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• 1994

This paper describes scheduling algorithms of the UNIX operating system and shows an analytical approach to approximate the average conditional response time for a process in the UNIX operating system. The average conditional response time is the average time between the submittal of a process requiring a certain amount of the CPU time and the completion of the process. The process scheduling algorithms in thr UNIX system are based on the priority service disciplines. That is, the behavior of a process is governed by the UNIX process schuduling algorithms that (ⅰ) the time-shared computer usage is obtained by allotting each request a quantum until it completes its required CPU time, (ⅱ) the nonpreemptive switching in system mode and the preemptive switching in user mode are applied to determine the quantum, (ⅲ) the first-come-first-serve discipline is applied within the same priority level, and (ⅳ) after completing an allotted quantum the process is placed at the end of either the runnable queue corresponding to its priority or the disk queue where it sleeps. These process scheduling algorithms create the round-robin effect in user mode. Using the round-robin effect and the preemptive switching, we approximate a process delay in user mode. Using the nonpreemptive switching, we approximate a process delay in system mode. We also consider a process delay due to the disk input and output operations. The average conditional response time is then obtained by approximating the total process delay. The results show an excellent response time for the processes requiring system time at the expense of the processes requiring user time.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Agribusiness and Information Management
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• v.1 no.2
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• pp.43-60
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• 2009

To improve the performance of wide-issue superscalar processors, it is essential to increase the width of instruction fetch and the issue rate. Removal of control hazard has been put forward as a significant new source of instruction-level parallelism for superscalar processors and the conditional branch prediction is an important technique for improving processor performance. Branch mispredictions, however, waste a large number of cycles, inhibit out-of-order execution, and waste electric power on mis-speculated instructions. Hence, the branch predictor with higher accuracy is necessary for good processor performance. In global-history-based predictors like gshare and GAg, many mispredictions come from commit update of the branch history. Some works on this subject have discussed the need for speculative update of the history and recovery mechanisms for branch mispredictions. In this paper, we present a new mechanism for recovering the branch history after a misprediction. The proposed mechanism adds an age_counter to the original predictor and doubles the size of the branch history register. The age_counter counts the number of outstanding branches and uses it to recover the branch history register. Simulation results on the SimpleScalar 3.0/PISA tool set and the SPECINT95 benchmarks show that gshare and GAg with the proposed recovery mechanism improved the average prediction accuracy by 2.14% and 9.21%, respectively and the average IPC by 8.75% and 18.08%, respectively over the original predictor.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.5
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• pp.1310-1321
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• 1994

Turbulent coherent structures in the intermediate wake of a stationary and rotating cylinder, spin rate S=0.7, situated in a uniform were experimentally investigated using a conditionalphase average technique. Measurements were carried out at a section of 8.5 diameters downstream form the center of cylinder and a Reynolds number of $Re=6.5{\times}10^{3}.$/TEX> The phase averaged velocity and velocity vector fields, contours of vorticity, turbulent intermittency function and velocity fluctuation energy are presented and discussed in relation to the large scale coherent structures by Karman vortices that shed periodically from the cylinder. Coherent wake structures of the rotating cylinder is almost identical with stationary cylinder, but the lateral displacement and shrinkage of turbulent wake region is occured by rotation. Rotation of the cylinder result in that the deflection of wake center to deceleration region(Y/D${\simeq}-0.3)$ and the decrease of mean velocity defect(10%), vorticity strength of large scale structures(19%), total velocity fluctuation energy(12%).

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.88-96
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• 1994

This paper proposes a new vector quantization scheme which exploits high correlations among indexes in vector quantization. An optimal vector quantizer in the rate-distortion sense can be obtained, if it is designed so that the average distortion can be minimized under the constraint of the conditional entropy of indes, which is usually much smaller than the entropy of index due to the high correlations among indexes of neighboring vectors. The oprimization process is very similar to that in ECVQ(entropy-constrained vector quanization) except that in the proposed scheme the Viterbi algorithm is introduced to find the optimal index sequence. Simulations show that at the same bitrate the proposed method provides higher PSNR by 1.0~3.0 dB than the conventional ECVQ when applied to image coding.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.26-30
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• 2018

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.