• Journal of the Mineralogical Society of Korea
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• v.28 no.4
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• pp.325-332
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• 2015

Apophyllite (KF)($K_{0.84}Ca_{3.99}Si_{7.70}O_{20}F_{0.72}{\cdot}8H_2O$), one of the sheet silicates, was compressed up to 7.7 GPa at ambient temperature and 15 high pressure data were obtained. Lattice parameters of the starting specimen were as follows: $a_0=8.954(2)\;{\AA}$, $c_0=15.795(2)\;{\AA}$, $V_0=1266.4(4)\;{\AA}^3$. Symmetrical diamond anvil cell was employed with synchrotron radiation in the mode of angular dispersive X-ray diffraction. Bulk modulus was determined to be 59(4) GPa when ${K_0}^{\prime}$ is 4. No clear first order phase transition symptom was observed in the series of XRD pattern. However, second-order phase transition cannot be ruled out from the correlation between normalized pressure and strain.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.

• Journal of Nutrition and Health
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• v.39 no.8
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• pp.721-727
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• 2006

This study was conducted to investigate the effects of Aralia elata, Acanthopanacis cortex and Ulmus davidiana water extracts on blood hemoglobin, $HbA_{1c}$, levels and biomarkers in the streptozotocin (STZ)-induced diabetic rats. Male Wistar rats divided into normal and diabetic groups. The diabetic groups subdivided into the control group (DM) and Araliaceae water extracts supplemented groups: Aralia elata (AE), Acanthopanacis cortex (AC) and Ulmus davidiana (UD). The extracts were supplemented in diet base on 11.42 g of raw Araliaceae/kg diet for 7 weeks. The diabetes was induced by injecting 572 (55 mg/kg B.W., i.p.) once 2 weeks before sacrifying. Relative weights of liver were significantly lowered in the DM group compared to the normal group, whereas those of kidney and heart were significantly increased in the DM group. Supplementation of the Araliaceae water extracts improved reduced liver weights in STZ-induced diabetic rats. Blood glucose level was significantly higher in the DM group than in the normal group, whereas insulin contents were significantly lowered in the DM groups. However, these parameters were normalized in the An, AC and UD supplemented groups, respectively. Blood hemoglobin and $HbA_{1c}$ levels were significantly higher in the DM group than in the normal group. When all of Araliaceae water extracts were supplemented to the diabetic rats lowered hemoglobin and $HbA_{1c}$ levels. Red blood cell, white blood cell and Lymphocyte were significantly higher in the DM group than in the normal group. The supplementation of Araliaceae family water extracts significantly lowered these parameters compared to the DM group. MCV, MCH contents were declined in the DM group, while the supplementation of Aralia elata, Acanthopanacis cortex and Ulmus davidiana water extracts elevated of these contents in STZ-induced diabetic rats. Accordingly, these results indicate that Aralia elata, Aeanthopanacis corex and Ulmus davidiana water extracts would seem to improve the blood biomarkers in STZ-induced diabetic rats.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.5
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• pp.49-59
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• 2008

The effects of design parameters on the pellet transport rate in the ejector system which is widely used in the production processes of automotive parts were investigated experimentally. The primary nozzle geometry, the area ratio (R) of nozzle exit cross-sectional area to mixing chamber cross-sectional area and the distance (S) from primary nozzle exit to mixing chamber entrance were considered as the design parameters. The area ratios of the primary nozzle were varied from R=0.10 to R=0.25, 0.30, 0.40 and 0.55. The primary nozzle was positioned at the non-dimensional distance (S/D) of 1.30, 1.87, 2.44, 3.00 and 3.75, normalized using the mixing chamber diameter (D). The design parameters were determined to run with high efficiency by measuring the pellets transport rate. The geometry and the area ratio (R) of the primary nozzle had an effect on the pellet transport rate of the ejector system, and the area ratio of R=0.3 was carefully selected after taking the minimum fluidization velocity and transport rate of applied pellets into account. The higher pellet transport rate with the variation of the distance (S/D) was observed at S/D of 2.44.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• Journal of Yeungnam Medical Science
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• v.39 no.1
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• pp.24-30
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• 2022

Background: Texture analysis has been used as a method for quantifying image properties based on textural features. The aim of the present study was to evaluate the usefulness of magnetic resonance imaging (MRI) texture analysis for the evaluation of viable ovarian tissue on the perfusion map of ovarian endometriosis. Methods: To generate a normalized perfusion map, subtracted T1-weighted imaging (T1WI), T1WI and contrast-enhanced T1W1 with sequences were performed using the same parameters in 25 patients with surgically confirmed ovarian endometriosis. Integrated density is defined as the sum of the values of the pixels in the image or selection. We investigated the parameters for texture analysis in ovarian endometriosis, including angular second moment (ASM), contrast, correlation, inverse difference moment (IDM), and entropy, which is equivalent to the product of area and mean gray value. Results: The perfusion ratio and integrated density of normal ovary were 0.52±0.05 and 238.72±136.21, respectively. Compared with the normal ovary, the affected ovary showed significant differences in total size (p<0.001), fractional area ratio (p<0.001), and perfusion ratio (p=0.010) but no significant differences in perfused tissue area (p=0.158) and integrated density (p=0.112). In comparison of parameters for texture analysis between the ovary with endometriosis and the contralateral normal ovary, ASM (p=0.004), contrast (p=0.002), IDM (p<0.001), and entropy (p=0.028) showed significant differences. A linear regression analysis revealed that fractional area had significant correlations with ASM (r²=0.211), IDM (r²=0.332), and entropy (r²=0.289). Conclusion: MRI texture analysis could be useful for the evaluation of viable ovarian tissues in patients with ovarian endometriosis.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.3
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• pp.172-180
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• 1998

It is attempted to verify the Quantitative relationship between fracture mechanical parameters (${\Delta}K$, $K_{max}$) and X-ray parameters (residual stress, half-value breadth) of A12009-15v/o $SiC_w$ composite, and normalized SS41 steel. In this study, fatigue crack propagation test were carried out and X-ray diffraction was applied to fatigue fractured surface in order to investigate the change of residual stress and half-value breadth on fatigue fractured surface. And it is loaded prestrain to each tensile specimen, A12009-15v/o $SiC_w$ composite(0.3, 0.5, 1, 1.5, 2%) and normalized SS41 steel(0.63, 2.25, 7.50, 13.7, 20%), for investigating plastic strain rate using nondestructive measurement method. X-ray diffraction was applied to the prestrained tensile specimens in order to measure the change of residual stress and half-value breadth.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.41-50
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• 2002

RPM is the one of the sensor models which is proposed by Open GIS Consortium (OGC) as image transfer standard. And it is the sensor model for end-users using IKONOS, a commercial pushbroom satellite, imagery which provide about 1m ground resolution. Parameters called RPC which is IKONOS RFM coefficients are serviced to end-users. But if some users try to make additional effort to get rigorous geo-spatial information, it is necessary to apply mathematic or abstract sensor models, because vendors don't offer any ancillary data for physical sensor models such as satellite orbit and navigation. Abstract sensor models such as pushbroom Direct Linear Transform (DLT) require many GCPs well distributed in imagery, and mathematic sensor model such as RFM, polynomials need much more GCPs. Therefore RPC modification using additional a few GCPs is the best solution. In this paper, two methods are proposed to modify RPC. One is method to use pseudo GCPs generated in normalized cubic, and another method uses parameters observations and a few GCPs. Through two methods, we get improvement of accuracy 50% and over.

• Journal of the Korean Geotechnical Society
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• v.15 no.3
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• pp.131-149
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• 1999

It was found that surface roughness has a first-order effect on the interface shear strength and accordingly it should be accurately quantified if its role is to be properly understood. To quantify the surface topography, first of all, a variety of commonly used surface roughness parameters and profiling methods were reviewed in this study. Based on this review, the normalized roughness parameter. $R_n$(Uesugi and Kishida, 1986), the profile roughness parameter, $R_L$, and the surface roughness parameter, $R_n$(Dove and Frost, 1996), were selected to be appropriate candidates of roughness parameters and the digital image analysis based Optical Profile Microscopy(OPM) method(Dove and Frost, 1996) to be an appropriate profiling method for this study. Using a smooth and three textured HDPE geomembranes which encompass the range of textures and texture patterns commonly used, a series of roughness measurements on virgin and previously used geomembranes were performed. The results showed that both $R_L\; and\; R_S$ values appropriately reflect the degree of texturing for the geomembranes used in this study, however, $R_n$ value showed limited ranges of variation which may not be sufficient to permit distinction between roughness values for certain conditions. The results of this study will be extended to the investigation of the influence of surface roughness on interface strength in future study.