• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• The Korean Journal of Nuclear Medicine
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• v.35 no.3
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• pp.131-141
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• 2001

Purpose: We evaluated the sensitivity of the F-18 FDG PET by visual assessment and statistical parametric mapping (SPM) analysis for the localization of the epileptogenic zones in frontal lobe epilepsy. Materials and Methods: Twenty-four patients with frontal lobe epilepsy were examined. All patients exhibited improvements after surgical resection (Engel class I or II). Upon pathological examination, 18 patients revealed cortical dysplasia, 4 patients revealed tumor, and 2 patients revealed cortical scar. The hypometabolic lesions were found in F-18 FDG PET by visual assessment and SPM analysis. On SPM analysis, cutoff threshold was changed. Results: MRI showed structural lesions in 12 patients and normal results in the remaining 12. F-18 FDG PET correctly localized epileptogenic zones in 13 patients (54%) by visual assessment. Sensitivity of F-18 FDG PET in MR-negative patients (50%) was similar to that in MR-positive patients (67%). On SPM analysis, sensitivity decreased according to the decrease of p value. Using uncorrected p value of 0.05 as threshold, sensitivity of SPM analysis was 53%, which was not statistically different from that of visual assessment. Conclusion: F-18 FDG PET was sensitive in finding epileptogenic zones by revealing hypometabolic areas even in MR-negative patients with frontal lobe epilepsy as well as in MR-positive patients. SPM analysis showed comparable sensitivity to visual assessment and could be used as an aid in the diagnosis of epileptogenic zones in frontal lobe epilepsy.

• Korean Journal of Environmental Agriculture
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• v.22 no.2
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• pp.79-86
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• 2003

This research was conducted to present reference data to be used as newly reclaimed tidal land management. We investigated vegetation succession at 4 reclaimed/reclaiming project areas and discussed relationship with soil and vegetation trhrough investigation and analysis soil chemical characteristics at 2 areas. 14 families 58 kinds were investigated. Vegetation were variou at Dea-Ho conservation polt and Seok-Mun National Industrial Area which are maintaining naturally. Vegetation were simple at Hong-Bo and Dongjin and MinKyong river areas which effected sea water. Common species that were investigated at 9 sites were Suaeda asparagoides, Aster tripolium, Phragmites australis, Suaeda maritima, Suaeda japonica, Carex scabrifolis. As soil desalinization progressing, soil classified at first saline-soidc soil, the nest saline soil and then normal soil. Chenopodiaceae revealed at about 30 dS/m of soil ECe and existed to 10 dS/m of soil ECe. At about 20 dS/m of soil ECe. Aster tripolium, Calamagrostis epigeios, and Sonchus brachyotus revealed and then non-halophytes and common plants at inland revealed at low soil ECe of about 10 dS/m. However it was not to progress vegetation sucdession and soil desalinization at the same time, owing to input of seeds or plants ect from out-ecosystem. So for promotion of vegetation at newly reclaimed tidal land, we proposed that it was very effective to plant artificially halophytes or suitable species through soil test.