• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.4
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• pp.87-92
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• 2013

In this study, we carried out the turning of plastic mold steel(STAVAX) with whisker reinforced ceramic tool(WA1) and analyzed ANOVA(Analysis of Variance) test. Multi-regression analysis was performed to find influential factors to surface roughness and to derive regression equation. Results are follows: From ANOVA test and confidence interval analysis of surface roughness, We found that influential factors to surface roughness was feed rate, cutting speed and depth of cut in order. From multi-regression analysis, we derived regression equation of STAVAX. it's coefficient of determination($R^2$) was 0.945 and It means that regression equation is significant. From experimental verification, we confirmed that surface roughness was predictable by regression equation. Compared with former research, we confirmed that increase of feed rate is the main cause of the growing of surface roughness and cutting force.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• Knowledge Management Research
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• v.16 no.4
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• pp.1-16
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• 2015

This paper aims to explore the small and medium sized enterprises'(SMEs') technological innovation through an open innovative strategy. Researchers have identified open innovation as external search 'breadth' and 'depth'. Although an open innovation strategy is well known as an effective way for SMEs' innovation, this stream of research examines differences between pursuing breadth of external knowledge and depth of external knowledge for SEMs' innovation. The sample comprises a total of 1106 SMEs included in the Korean Innovation Survey, and logistic regression analysis and odds ratio comparison were used to evaluate the relationship between external knowledge search and innovation outcomes. The results show that both 'breadth' and 'depth' positively affect the SMEs' innovation. When SMEs are simultaneously pursuing external searching for breadth and depth, however, a negative result on innovation outcome followed because of the lack of their internal resources and capacities. Despite these contributions, we have certain limitations that can be regarded as means of future research. Even though breadth and depth are adopted to measure the way of how a firm sources the external knowledge, companies may place the different weight on each source of knowledge. And also, it is difficult to understand how the knowledge gained through external search contributes to a firm's incremental and radical innovation, respectively.

• Proceedings of the KSRS Conference
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• v.2
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• pp.525-528
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• 2006

The remote sensing data can be used to calculate the water depth especially in the clear and shallow water area. In this study, the SPOT data was used for bathymetric mapping in Dong-Sha atoll, located in northern South China Sea. The in situ sea depth was collected by echo sounder as well. A global positioning system was employed to locate the accurate sampling points for sea depth. An empirical model between measurement sea depth and band digital count was determined and based on least squares regression analysis. Both non-classification and unsupervised classification were used in this study. The results show that the standard error is less than 0.9m for non-classification. Besides, the 10% error related to the measurement water depth can be satisfied for more than 85% in situ data points. Otherwise, the 10% relative error can reach more than 97%, 69%, and 51% data points at class 4, 5, and 6 respectively if supervised classification is applied. Meanwhile, we also find that the unsupervised classification can get more accuracy to estimate water depth with standard error less than 0.63, 0.93, and 0.68m at class 4, 5, and 6 respectively.

• Korean Journal of Agricultural Science
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• v.42 no.3
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• pp.183-189
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• 2015

Plant root penetration through soil profile is restricted by compacted layer such as plow pan under conventional tillage. For detecting the compact layer, we made a graduated T-shape probe and measured compared between the depths with rapid change in feeling hardness of hand penetration using T-shape probe and with a rapid increase of penetrometer cone index. On upland crops, including red pepper, corn, soybean and cucumber, plow pan depth ranged from 10 cm to 25 cm depth. The effective rooting depth (ER) had significant correlation with the plow pan depth (PP) except soils with the shallow ground water and/or poorly drained soil. The regression equation was ER = 0.9*PP ($R^2=0.54^{**}$, N = 14) with the applicative PP range of 10-25 cm.

• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.211-218
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• 2007

In this study, vertical electrical sounding (VES) data and ordinary kriging are used to identify the alluvial depth of each area that Korea Resources Corporation (KORES) conducted groundwater survey at Miryang area in Gyeongsangnam-do and Pocheon area in Gyeonggi-do from 2003 to 2004. To verify the applicability of VES data to ordianry kriging, regression analysis of VES data versus drillhole data is conducted. Comparing the alluvial depth distributions using ordinary kriging with existing drillhole data, the result shows that the depth distributions are reasonably depicted along with the topography and the basin. So, the ordinary kriging of VES data is useful to identify the alluvial depth distributions.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.20-25
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• 2004

Using the multiple regression analysis cutting forces of turning processes have been predicted based on the cutting conditions such as feed rate(f), depth of cut(d), and cutting velocity(v). The statistical inference of the equation was checked by ANOVA test. The validity of the proposed regression analysis was verified by two sets of cutting tests of 27 cutting conditions and the additional cutting tests of 18 cutting conditions. From the results of analytical and experimental studies, it was found that there was no significant difference between the measured and predicted cutting forces. Also, the shear and friction characteristics of turning processes were analyzed with predicted cutting forces.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.37-42
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• 2004

End-milling have been widely used in aircraft, automobile part and moulding industry. However, various working factors such as spindle speed, feed rate and depth of cut in end-milling have an effect on spindle vibration. There it is demanded the quantitative analysis of spindle vibration in order to get the optimum surface roughness. This study was carried out to analyze an influence of working factors on spindle vibration by design of Experiment. The results are shown that mathematical model of regression equation for an influence of working factors on vibration acceleration of spindle in end-milling by regression analysis is presented.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1148-1153
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface rougj1ness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on Spindle speed, feed rale, hardness and axial depth of cut By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 89.7% and regression equation of surface roughness is made by regression analysis.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.9-11
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• 2016

Purpose In this paper we proposed bi-directional hierarchical regression for accurate human finger landmark detection with only using depth information.Materials and Methods Our algorithm consisted of two different step, initialization and landmark estimation. To detect initial landmark, we used difference of random pixel pair as the feature descriptor. After initialization, 16 landmarks were estimated using cascaded regression methods. To improve accuracy and stability, we proposed bi-directional hierarchical structure.Results In our experiments, the ICVL database were used for evaluation. According to our experimental results, accuracy and stability increased when applying bi-directional hierarchical regression more than typical method on the test set. Especially, errors of each finger tips of hierarchical case significantly decreased more than other methods.Conclusion Our results proved that our proposed method improved accuracy and stability and also could be applied to a large range of applications such as augmented reality and simulation surgery.