• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• Applied Chemistry for Engineering
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• v.9 no.2
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• pp.232-237
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• 1998

This study was carried out to find the optimal operating conditions for separation of residual uranium from the simulated radwaste solution containing 19 elements, and to evaluate the validity of the process. The selected process was based on the solvent extraction with TBP(tributyl phosphate). As an extractor, two miniature mixer-settlers with a total of 18 stages were used. Extraction yield of U, Np and Tc was about 99.2%. 32.1%, and 99.9%, respectively. The other elements were coextracted in the range of 1~4%. Extraction yield of U exceeded those of the previous work performed with batch system, which resulted in the low extractability of U (about 80%) according to the coexisting element such as Nd and Fe. It was due to the characteristics of multi-stage extractor. On the other hand, low extractability of Np was caused by various oxidation states in the nitric acid medium. In the case of Tc, its high extractability may be attributed to the complex formation with Zr and U, which is not well proved yet. All elements extracted with TBP were stripped into aqueous phase more than 99% by 0.01M $HNO_3$. From the results, this process has no problem with respect to in the same step was required, because Np was distributed in the raffinate and U product, respectively.

• Journal of the korean academy of Pediatric Dentistry
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• v.34 no.4
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• pp.579-589
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• 2007

The objective of this study was to compare the shear bond strengths of five adhesive systems to the enamel and dentin of primary and permanent teeth. Fifty noncarious primary and fifty permanent teeth were collected and stored in an 0.1% thymol solution at room temperature after extraction. The tested adhesives were: Adper Scotchbond Multi-purpose Plus Adhesive (SM) Adper Single bond 2 (SB), Clearfil SE Bond (SE), Adper Prompt L-Pop (PL), GBond (GB). For the shear bonding test, the labial and lingual surfaces of primary and permanent teeth were used. To obtain a flat surface, the labial and lingual surfaces of the teeth were sanded on $SiO_2$ with number 600 grit and then divided into 20 groups of 10 surfaces each. All samples were theromocycled in water $5^{\circ}C$ and $55^{\circ}C$ for 1000 cycles. The results were as follows: 1. For primary enamel, shear bond strengths of SM and SB were significantly higher than that of SE and also SM, SB, and PL were higher than GB(p<0.05). 2. For primary dentin, there were no significant differences among the shear bond strengths of any other bonding systems except difference between SE and GB. 3. For permanent enamel, SB showed significantly higher mean shear bond strength than those of any other bonding systems(p<0.05). 4. For permanent dentin, SM showed significantly higher mean shear bond strength than that of PL and GB(p<0.05). 5. Between the primary enamel and dentin, there were significant differences in SM, SB, and GB, whereas there was statistically significant difference in PL between the permanent enamel and dentin(p<0.05). 6. Between the primary and permanent teeth on enamel, there were no significant differences among all bonding systems, whereas there were statistically significant differences in SM and SB between the primary and permanent teeth on dentin(p<0.05).

• Journal of Korean Society for Atmospheric Environment
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• v.8 no.4
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• pp.247-256
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• 1992

Airborne suspended particulates were collected at Shinchon by a high volume cascade impactor from Sep. 1990 to Aug. 1991. Organic matter was extracted from particulates and fractionated by liquid-liquid extraction and thin layer chromatography. Substances in the PAHs and nitroarenes'subfraction of neutral fraction were determined by capillary gas chromatography. Based on unit risk estimates by multi-stage model of benzo[a]pyrene and the results of exposure estimates, cancer risk was assessed. The annual average concentration of total suspended particulates was 201.77g/$m^3$. The percentage of fine particulates was 57.40. The concentration of total suspended particulates showed seasonal variations and was high in winter and spring. The average concentration of extractable organic matter was 8.12g/$m^3$. In all, 21 PAHs were identified and quantified. The annual concentration of fluoranthene was 2.38ng/$m^3$, and that was the highest value of all PAHs. A carcinogenic compound, benzo[a]pyrene, was at a concentration of 1.84ng/$m^3$. All the 10 nitroarenes were also identified and quantified. The major nitroarene in the Shinchon area was 2,7-dinitrofluorene. The annual concentration of 1-nitropyrene was 1.56ng/$m^3$. Concentrations of PAHs and nitroarenes were high in winter and low in summer. The life time excess risk estimates of benzo[a]pyrene was calculated as 0.96 persons/a million population in this experiment. In the rank of relative potenties, carcinogenic effects of the other PAHs were calculated as 0.004-0.108 persons/a million population.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• The Korean Journal of Pesticide Science
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• v.14 no.4
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• pp.361-370
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• 2010

Considering the efficiencies of the preparation process at each stage obtained in previous studies, the analytical determination method was established for multi-pesticide residues in soils. It consist of the acetone-extraction, the dichloromethane-partition, the Florisil or silica-gel chromatography and the gas chromatography analysis equipped with the electron capture detector and the nitrogen-phosphorus detector. In the soil recovery test by Florisil clean-up system, the number of pesticides recovered in the range of 70~120% and showed less than 20% of RSD were 165 pesticides for paddy soil, 169 pesticides for upland soil and 159 pesticides in both soils through the tested 183 pesticides. And in the soil recovery test by silica-gel system, the number of pesticides recovered in the range of 70~120% and showed less than 20% of RSD were 154 pesticides for paddy soil, 145 pesticides for upland soil, and 134 pesticides in both soils.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.1-11
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• 2009

Rapid visualization of flood area of downstream according to the dam effluent in flood season is very important in dam management works. Overlay zone of river bight should be removed to represent flood area efficiently based on flood stage which was modeled in river channels. This study applied drainage enforcement algorithm to visualize flood area considering river bight by coupling Coordinate Operation System for Flood control In Multi-reservoir (COSFIM) and Flood Wave routing model (FLDWAV). The drainage enforcement algorithm is a kind of interpolation which gives to advantage into hydrological process studies by removing spurious sinks of terrain in automatic drainage algorithm. This study presented mapping technique of flood area layer considering river bight in Namgang-Dam downstream, and developed system based on Arcobject component to execute this process automatically. Automatic extraction system of flood area layer could save time-consuming efficiently in flood inundation visualization work which was propelled based on large volume data. Also, flood area layer by coupling with IKONOS satellite image presented real information in flood disaster works.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.38-46
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• 2012

Atrial fibrillation is needed to be detected at paroxysmal stage and to be treated. But, paroxysmal atrial fibrillation ECG is hardly obtained with 12-lead electrocardiographs but Holter systems. Presently, the averaged beat subtraction(ABS) method is solely used to estimate atrial fibrillatory waves even with somewhat large residual error. As an alternative, in this study, we suggested an ESAF(event-synchronous adaptive filter) based algorithm, in which the AF ECG was treated as a primary input and event-synchronous impulse train(ESIT) as a reference. And, ESIT was generated so to be synchronized with the ventricular activity by detecting QRS complex. We tested proposed algorithm with simulated AF ECGs and real AF ECGs. As results, even with low computational cost, this ESAF based algorithm showed better performance than the ABS method and comparable performance to algorithm based on PCA(principal component analysis) or SVD(singular value decomposition). We also proposed an expanded version of ESAF for some AF ECGs with multi-morphologic ventricular activities and this also showed reasonable performance. Ultimately, with Holter systems including our proposed algorithm, atrial activity signal can be precisely estimated in real-time so that it will be possible to calculate atrial fibrillatory rate and to evaluate the effect of anti-arrhythmic drugs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.