• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.67-72
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• 2005

Most of image binarization algorithms analyzes the intensity distribution using the histogram for the determination of threshold value. When the intensity difference between the foreground object and the background is great, the histogram shows the tendency to be bimodal and the selection of the histogram valley as the threshold value shows the good result. On the other side. when the intensity difference is not great and the histogram doesn't show the bimodal property, the histogram analysis doesn't support the selection of the proper threshold value. This Paper Proposed the novel binarization method that applies the fuzzy membership function to each color value on the RGB color model and, by using the operation results, separates the features having the great readability from the background. The proposed method prevents the loss of information incurred by the gray scale conversion by using the RGB color model and extracts effectively the readable features by using the fuzzy inference Compared with the traditional binarization methods, the proposed method is able to remove the majority of noise areas and show the improved results on the image of transport containers , etc.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.43-46
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• 1994

A new spatial localization technique using high-order surface gradient coil (SGC) is proposed. Although the Spatial Selection with High-Order gradient (SHOT) can provide a 2-D selection with only one selective RF pulse, the high-order gradient produced by cylindrical-shape coils has not been clinically useful for clinical systems due to the large minimum selection size caused by the limited radial gradient intensity. However, by using the proposed high-order SGCs located near the imaging region, the size of volume selection can be reduced to a clinically useful 1-4 cm in diameter by applying stronger radial gradient with much less gradient driving power. A 40 cm-by-40 cm $r^{2}$ SGC has been designed and constructed, and phantom and volunteer studies have been performed. Experimental results using spatially localized MRI show good agreement to the theoretically predicted behavior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.3
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• pp.553-563
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• 1996

Detecting edges is one of issues with essentialimprotance in the area of image analysis. An edge in an image is a boundary or contour at which a significant change occurs in image intensity. Edge detection has been studied in many addlications such as imagesegmentation, robot vision, and image compression. In this paper, we propose an automatic threshold selection scheme for edge detection and show its application to noise elimination. The scheme suggested here applied statistical properties of the noise estimated from a noisy image to threshold selection. Since a selected threshold value in the scheme depends on not the characgreistic of an orginal image but the statistical feature of added noise, we can remove ad-hoc manners used for selecting the threshold value as well as decide the value theoretically. Furthermore, that shceme can reduce the number of edge pixels either generated or lost by noise. an application of the scheme to noise elimination is shown here. Noise in the input image can be eliminated with considering the direction of each edge pixedl on the edge map obtained by applying the threshold selection scheme proposed in this paper. Achieving significantly improved results in terms of SNR as well as subjective quality, we can claim that the suggested method works well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Journal of Animal Science and Technology
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• v.55 no.6
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• pp.501-508
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• 2013

This study was conducted to determine genetic correlations among carcass traits measured by ultrasound and real carcass measurements and to estimate indirect selection responses for real carcass traits based on ultrasound measurements in Hanwoo cows. To accomplish this, 22,080 ultrasound measurement records from 17,926 cows collected from 2001 to 2012 and 11,907 carcass records obtained from fattened cattle from 2008 to 2012 were used. Genetic parameters were estimated based on eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) measured by ultrasound-scanning of live cows and using the official technique on chilled bovine half-carcasses after slaughtering. Heritability and genetic correlation for carcass traits were estimated using a mixed model equation that consisted of environmental effects as fixed parameters and additive genetic effects and residual effects as random parameters, assuming that traits were different between ultrasound and carcass measurements. This statistical method was applied to the average information restricted maximum likelihood method. The heritability of EMA, BF and MS measured by ultrasound were 0.33, 0.61 and 0.46, respectively, while the heritability estimates of the corresponding traits based on carcass measurements were 0.29, 0.40 and 0.38, respectively and the genetic correlation between ultrasound and carcass traits for EMA, BF and MS were 0.41, 0.78 and 0.67, respectively. The genetic correlation between ultrasound and carcass traits was highly positive. Additionally, the selection response for marbling score was estimated to be 0.42 per generation if the cows were selected based on the ultrasound scan marbling score with an assumed selection intensity of 0.8. Overall, these results indicate that the ultrasound scan technique would be applicable to judging cow selection for genetically improved meat quality.

• Journal of the Korean Wood Science and Technology
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• v.43 no.6
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• pp.721-729
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• 2015

Wood properties depend on not only environmental factors such as soil, climate change, or forest stand characteristics, but also silviculture practices such as thinning, regeneration, or selection. This study report influences of the extent of thinning intensity from no thinning, moderate and heavy thinning to the wood property of Quercus acuta forest stands in Wan-do arboretum, Jeollanam-do Province. The results showed that there were close relationships between thinning intensity and anatomical, physical or mechanical properties of Quercus acuta wood. Especially, there are close relationships between thinning intensity and ring width or mechanical properties of wood. As a result, this study showed high correlations between Quercus acuta wood properties and thinning intensity of Quercus acuta forest stand. These findings are expected to be very useful as fundamental data for the implementation of silviculture practices of this specie to produce timber.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.37-48
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• 2005

In this paper, a new object detection method for FLIR images is proposed. The proposed method consists of intensity ranking feature and a classification algerian using the feature. The intensity ranking feature is a representation of an image, from which intensity distribution is regularized. Each object candidate region is classified as object or non-object by the proposed classification algorithm which is based on the intensity ranking similarity between the candidate and object training images. Using the proposed algorithm pixel-wise detection results can be obtained without any additional candidate selection algorithm. In experimental results, it is shown that the proposed ranking feature is appropriate for object detection in a FLIR image and some vehicle detection results in the situation of existing noise, scale variation, and rotation of the objects are presented.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.4
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• pp.281-289
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• 2016

Spinyhead sculpin Dasycottus setiger, a species of cold water fish, is distributed along the eastern coastal waters of Korea. A series of fishing experiments was carried out in the waters near Uljin from June, 2002 to November, 2004, using the experimental monofilament gill nets of different mesh sizes (82.2, 89.4, 104.8, and 120.2 mm) to describe the selectivity of the gill net for the fish. The SELECT (Share Each Length's Catch Total) analysis with maximum likelihood method was applied to fit the different functional models (normal, lognormal, and bi-normal models) for selection curves to the catch data. The bi-normal model with the fixed relative fishing intensity was selected as the best-fit selection curve by AIC (Akaike's Information Criterion) comparison. For the best-fit selection curve, the optimum relative length (the ratio of fish total length to mesh size) with the maximum efficiency and the selection range ($R_{50%,large}-R_{50%,small}$) of 50% retention were obtained as 2.363 and 0.851, respectively. The ratios of body girth to mesh perimeter at 100% retention where the selection curve of each mesh size represented the optimum total length were calculated as the range of 0.86 ~ 0.87.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4513-4532
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• 2014

Motivated by the Weber's Law, this paper proposes an efficient and robust shape descriptor based on the perceptual stimulus model, called Weber's Law Shape Descriptor (WLSD). It is based on the theory that human perception of a pattern depends not only on the change of stimulus intensity, but also on the original stimulus intensity. Invariant to scale and rotation is the intrinsic properties of WLSD. As a global shape descriptor, WLSD has far lower computation complexity while is as discriminative as state-of-art shape descriptors. Experimental results demonstrate the strong capability of the proposed method in handling shape retrieval.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.