• Journal of Astronomy and Space Sciences
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• v.39 no.2
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• pp.43-50
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• 2022

The unified scheme of Seyfert galaxies hypothesizes that the observed differences between the two categories of Seyfert galaxies, type 1 (Sy1) and type 2 (Sy2) are merely due to the difference in the orientation of the toroidal shape of the obscuring material in the active galactic nuclei. We used in this paper, a sample consisting of 120 Seyfert galaxies at 1.40 × 10⁹ Hz in radio, 2.52 × 10¹⁷ Hz in X-ray and 2.52 × 10²³ Hz in γ-ray luminosities observed by the Fermi Large Area Telescope (Fermi-LAT) in order to test the unified scheme of radio-quiet Seyfert galaxies. Our main results are as follows: (i) We found that the distributions of multiwave luminosities (L_radio, L_X-ray, and L_γ-ray) of Sy1 and Sy2 are completely overlapped with up to a factor of 4. The principal component analysis result reveals that Sy1 and Sy2 also occupy the same parameter spaces, which agrees with the notion that Sy1 and Sy2 are the same class objects. A Kolmogorov-Smirnov test performed on the sub-samples indicates that the null hypothesis (both are from the same population) cannot be rejected with chance probability p ~ 0 and separation distance K = 0.013. This result supports the fact that there is no statistical difference between the properties of Sy1 and Sy2 (ii) We found that the coefficient of the best-fit linear regression equation between the common properties of Sy1 and Sy2 is significant (r > 0.50) which plausibly implies that Sy1 and Sy2 are the same type of objects observed at different viewing angle.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Journal of Life Science
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• v.28 no.2
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• pp.148-152
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• 2018

The patchiness of local environments within a habitat is assumed to be a primary factor affecting the spatial patterns of plants, and a randomization procedure is developed for testing the null hypothesis that only spatial association with patches determined the spatial patterns of plants. Chloranthus japonicus (Chloranthaceae) is an herbaceous perennial and a member of the genus Chloranthus in the family Chloranthaceae. The spatial pattern of C. japonicus was analyzed according to several patchiness indices, population uniformity or aggregation under different sizes of plots by dispersion indices, and spatial autocorrelation. Population densities (D) varied from 0.356 to 2.270, with a mean of 1.527. The values of dispersion indices ( at Mt. Ahop were lower than 1 at six plots ($2m{\times}2m$, $2m{\times}4m$, $4m{\times}4m$, $4m{\times}8m$, $8m{\times}8m$, and $8m{\times}16m$), but the two large plots ($16m{\times}16m$ and $16m{\times}32m$) were higher than 1. Thus, the aggregation indices ( were negative at Mt. Ahop, which indicates a uniform distribution. The two large plots ($16m{\times}16m$ and $16m{\times}32m$) had positive CIs. However, the values were not large (0.009 for the $16m{\times}16m$ plot and 0.038 for the $16m{\times}32m$ plot). The mean crowding ($M^{\ast}$) and patchiness index (PAI) showed positive values for all plots.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6673-6680
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• 2013

Objective: Insulin resistance (IR) is an established risk factor for colorectal cancer (CRC). Given that CRC and IR physiologically overlap and the calpain-10 gene (CAPN10) is a candidate for IR, we explored the association between CAPN10 and CRC risk. Methods: Blood samples of 400 case-control pairs were genotyped, and the lifestyle and dietary habits of these pairs were recorded and collected. Unconditional logistic regression (LR) was used to assess the effects of CAPN10 SNP43 and SNP19, and environmental factors. Both generalized multifactor dimensionality reduction (GMDR) and the classification and regression tree (CART) were used to test gene-environment interactions for CRC risk. Results: The GA+AA genotype of SNP43 and the Del/Ins+Ins/Ins genotype of SNP19 were marginally related to CRC risk (GA+AA: OR = 1.35, 95% CI = 0.92-1.99; Del/Ins+Ins/Ins: OR = 1.31, 95% CI = 0.84-2.04). Notably, a high-order interaction was consistently identified by GMDR and CART analyses. In GMDR, the four-factor interaction model of SNP43, SNP19, red meat consumption, and smoked meat consumption was the best model, with a maximum cross-validation consistency of 10/10 and testing balance accuracy of 0.61 (P < 0.01). In LR, subjects with high red and smoked meat consumption and two risk genotypes had a 6.17-fold CRC risk (95% CI = 2.44-15.6) relative to that of subjects with low red and smoked meat consumption and null risk genotypes. In CART, individuals with high smoked and red meat consumption, SNP19 Del/Ins+Ins/Ins, and SNP43 GA+AA had higher CRC risk (OR = 4.56, 95%CI = 1.94-10.75) than those with low smoked and red meat consumption. Conclusions: Though the single loci of CAPN10 SNP43 and SNP19 are not enough to significantly increase the CRC susceptibility, the combination of SNP43, SNP19, red meat consumption, and smoked meat consumption is associated with elevated risk.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.345-349
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• 2019

In Korea, the mountainous area occupies more than 70% of the whole country, cutting of earth slope that cuts a part of the ground surface is widely used when building infrastructures such as road, railroad, and industrial complex construction. In recent years, regulations on environmental damage have become more strict, and various methods have been developed and applied. Among them, Concrete-Panel Retaining Wall technique is actively applied. Concrete-Panel Retaining Wall is a method to resist horizontal earth pressure by forming a wall by attaching a precast retaining wall to the front of the support material and increasing the shear strength of the disk through reinforcement of the support material. Soil nailing, earth bolt, and ground anchor are used as support material. Among them, ground anchor is a more aggressive reinforcement type that introduces tensile load in advance to the steel wire, and a large concentrated load acts on the front panel. This concentrated load is a factor that creates cracks in the concrete panel and reduces the durability of the retaining wall itself. In this study, steel pipe sleeves and reinforcements were purchased at the anchorage of the panel to prevent cracks, and by applying bumpy shear keys to the end of the panel, the weakness of the individual behavior of the existing grout anchors was improved. The problem of degraded landscape by exposure to front concrete of retaining wall and protrusion of anchorage was solved by the production of natural stone patterns and the construction of sections that do not protrude the anchorage. In order to verify the effectiveness of anti-crack sleeves and reinforcements used in the null, indoor testing and three-dimensional numerical analysis have been performed, and the use of steel pipe sleeves and reinforcements has demonstrated the overall strength increase and crack suppression effect of panels.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.207-215
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• 2022

This research has been focused on accessing precision and accuracy of UAV (Unmanned Aerial Vehicle)-derived 3-D surveying coordinates. To this end, a highly precise and accurate testing control network had been established by GNSS (Global Navigation Satellite Systems) campaign and its network adjustment. The coordinates of the ground control points and the check points were estimated within 1cm accuracy for 95% of the confidence level. FC330 camera mounted on DJI Phantom 4 repeatedly took aerial photos of an experimental area seven times, and then processed them by two widely used software packages. To evaluate the precision and accuracy of the aerial surveys, 3-D coordinates of the ten check points which automatically extracted by software were compared with GNSS solutions. For the 95% confidence level, the standard deviation of two software's result is within 1cm, 2cm, and 4cm for the north-south, east-west, and height direction, and RMSE (Root Mean Square Error) is within 9cm and 8cm for the horizontal, vertical component, respectively. The interest is that the standard deviation is much smaller than RMSE. The F-ratio test was performed to confirm the statistical difference between the two software processing results. For the standard deviation and RMSE of most positional components, exception of RMSE of the height, the null hypothesis of the one-tailed tests was rejected. It indicates that the result of UAV photogrammetry can be different statistically based on the processing software.

• Journal of Distribution Science
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• v.8 no.3
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• pp.27-35
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• 2010

As the population, buying power, and intensity of self-expression of the elderly generation increase, its importance as a market segment is also growing. Therefore, the mass marketing strategy for the elderly generation must be changed to a micro-marketing strategy based on the results of sub-segmentation that suitably captures the characteristics of this generation. Furthermore, as a customer access strategy is decided by sub-segmentation, proper segmentation is one of the key success factors for micro-marketing. Segments or sub-segments are different from sectors, because segmentation or sub-segmentation for micro-marketing is based on the homogeneity of customer needs. Theoretically, complete segmentation would reveal a single voice. However, it is impossible to achieve complete segmentation because of economic factors, factors that affect effectiveness, etc. To obtain a single voice from a segment, we sometimes need to divide it into many individual cases. In such a case, there would be a many segments to deal with. On the other hand, to maximize market access performance, fewer segments are preferred. In this paper, we use the term "sub-segmentation" instead of "segmentation," because we divide a specific segment into more detailed segments. To sub-segment the elderly generation, this paper takes their lifestyles and life stages into consideration. In order to reflect these aspects, various surveys and several rounds of expert interviews and focused group interviews (FGIs) were performed. Using the results of these qualitative surveys, we can define six sub-segments of the elderly generation. This paper uses five rules to divide the elderly generation. The five rules are (1) mutually exclusive and collectively exhaustive (MECE) sub-segmentation, (2) important life stages, (3) notable lifestyles, (4) minimum number of and easy classifiable sub-segments, and (5) significant difference in voices among the sub-segments. The most critical point for dividing the elderly market is whether children are married. The other points are source of income, gender, and occupation. In this paper, the elderly market is divided into six sub-segments. As mentioned, the number of sub-segments is a very key point for a successful marketing approach. Too many sub-segments would lead to narrow substantiality or lack of actionability. On the other hand, too few sub-segments would have no effects. Therefore, the creation of the optimum number of sub-segments is a critical problem faced by marketers. This paper presents a method of evaluating the fitness of sub-segments that was deduced from the preceding surveys. The presented method uses the degree of homogeneity (DoH) to measure the adequacy of sub-segments. This measure uses quantitative survey questions to calculate adequacy. The ratio of significantly homogeneous questions to the total numbers of survey questions indicates the DoH. A significantly homogeneous question is defined as a question in which one case is selected significantly more often than others. To show whether a case is selected significantly more often than others, we use a hypothesis test. In this case, the null hypothesis (H0) would be that there is no significant difference between the selection of one case and that of the others. Thus, the total number of significantly homogeneous questions is the total number of cases in which the null hypothesis is rejected. To calculate the DoH, we conducted a quantitative survey (total sample size was 400, 60 questions, 4~5 cases for each question). The sample size of the first sub-segment-has no unmarried offspring and earns a living independently-is 113. The sample size of the second sub-segment-has no unmarried offspring and is economically supported by its offspring-is 57. The sample size of the third sub-segment-has unmarried offspring and is employed and male-is 70. The sample size of the fourth sub-segment-has unmarried offspring and is not employed and male-is 45. The sample size of the fifth sub-segment-has unmarried offspring and is female and employed (either the female herself or her husband)-is 63. The sample size of the last sub-segment-has unmarried offspring and is female and not employed (not even the husband)-is 52. Statistically, the sample size of each sub-segment is sufficiently large. Therefore, we use the z-test for testing hypotheses. When the significance level is 0.05, the DoHs of the six sub-segments are 1.00, 0.95, 0.95, 0.87, 0.93, and 1.00, respectively. When the significance level is 0.01, the DoHs of the six sub-segments are 0.95, 0.87, 0.85, 0.80, 0.88, and 0.87, respectively. These results show that the first sub-segment is the most homogeneous category, while the fourth has more variety in terms of its needs. If the sample size is sufficiently large, more segmentation would be better in a given sub-segment. However, as the fourth sub-segment is smaller than the others, more detailed segmentation is not proceeded. A very critical point for a successful micro-marketing strategy is measuring the fit of a sub-segment. However, until now, there have been no robust rules for measuring fit. This paper presents a method of evaluating the fit of sub-segments. This method will be very helpful for deciding the adequacy of sub-segmentation. However, it has some limitations that prevent it from being robust. These limitations include the following: (1) the method is restricted to only quantitative questions; (2) the type of questions that must be involved in calculation pose difficulties; (3) DoH values depend on content formation. Despite these limitations, this paper has presented a useful method for conducting adequate sub-segmentation. We believe that the present method can be applied widely in many areas. Furthermore, the results of the sub-segmentation of the elderly generation can serve as a reference for mature marketing.