• Journal of the Korean Geotechnical Society
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• v.15 no.6
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• pp.121-130
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• 1999

The objective of this study is to perform finite element analyses(FEA) using the anisotropic hardening constitutive model on the basis of the total stress concept. An anisotropic hardening model was then developed to solve the problem and its mathematical formulations and experimental verifications were also described. In a companion paper, the constitutive equation will be formulated for accurate and efficient solutions of FEA, and coded into a nonlinear analysis program, and finally a field problem will be analyzed. The proposed model includes the failure criterion of a von Mises type and the anisotropic hardening rule based on the generalized isotropic hardening description, which can model the nonlinearity and the anisotropy of the stress-strain relationship. As a result this study could verty the experimental results for UU triaxial tests, CU triaxial tests for overconsolidated samples, and anisotropic loading tests with the rotation of principal stress axes for $K_0$consolidated samples.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• Journal of Food Hygiene and Safety
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• v.36 no.6
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• pp.504-509
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• 2021

The objective of this study was to develop dynamic model to describe the kinetic behavior of E. coli in sliced smoked duck. E. coli was detected in 2 sliced smoked duck samples (16.7%) at 1.23 log CFU/g. The maximum specific growth rate (𝜇_max) of E. coli ranged from 0.05 to 0.36 log CFU/g/h, and lag phase duration (LPD) ranged from 4.39 to 1.07 h, depending on the storage at 10-30℃, and h₀ value ranged from 0.24 to 0.51. The developed model was validated with observed values obtained at 13℃ and 25℃. The model performance was appropriate with 0.130 of root mean squared error (RMSE), and the dynamic model also described properly kinetic behavior of E. coli in sliced smoked duck samples. These results indicate that E. coli can contaminate sliced smoked ducks and the models developed with the E. coli isolates are useful in describing the kinetic behavior of E. coli in sliced smoked duck.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.547-571
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• 2023

To deeply probe the actual earthquake level and fragility of typical reinforced concrete (RC) structures under multiple intensity grades, considering diachronic measurement building stock samples and actual observations of representative catastrophic earth shocks in China from 1990 to 2010, RC structures were divided into traditional RC structures (TRCs) and bottom reinforced concrete frame seismic wall masonry (BFM) structures, and the empirical damage characteristics and mechanisms were analysed. A great deal of statistics and induction were developed on the historical experience investigation data of 59 typical catastrophic earthquakes in 9 provinces of China. The database and fragility matrix prediction model were established with TRCs of 4,122.5284×104 m² and 5,844 buildings and BFMs of 5,872 buildings as empirical seismic damage samples. By employing the methods of structural damage probability and statistics, nonlinear prediction of seismic vulnerability, and numerical and applied functional analysis, the comparison matrix of actual fragility probability prediction of TRC and BFM in multiple intensity regions under the latest version of China's macrointensity standard was established. A novel nonlinear regression prediction model of seismic vulnerability was proposed, and prediction models considering the seismic damage ratio and transcendental probability parameters were constructed. The time-varying vulnerability comparative model of the sample database was developed according to the different periods of multiple earthquakes. The new calculation method of the average fragility prediction index (AFPI) matrix parameter model has been proposed to predict the seismic fragility of an areal RC structure.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.31-38
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• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• Polymer(Korea)
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• v.38 no.4
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• pp.449-456
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• 2014

Ternary blends of poly(L-lactic acid) (PLLA), poly(${\varepsilon}$-caprolactone) (PCL) and polyethylene oxide (PEO) were produced with different concentrations of components via melt blending. By leaching the PEO from the samples by water, porous materials were obtained with potential application for bio scaffolds. Sample porosity was evaluated by calculating the ratio of porous scaffold density (${\rho}^*$) to the non-porous material density (${\rho}_s$). Highest porosity (51.42%) was related to the samples containing 50 wt%. of PEO. Scanning electron microscopy (SEM) studies showed the best porosity resulted by decreasing PLLA/PCL ratio at constant concentration of PEO. Crystallization behavior of the ternary blend samples was studied using differential scanning calorimetry (DSC). Results revealed that the crystallinity of PLLA was improved by addition of PEO and PCL to the samples. The porosity plays a key role in governing the compression properties. Mechanical properties are presented by Gibson-Ashby model.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.514-526
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• 2013

With a long sensing period, the inter-frame spectrum sensing in IEEE 802.22 standard is vulnerable to the effect of the traffic of the primary user (PU). In this article, we address the two degrading factors that affect the inter-frame sensing performance with respect to the random arrival/leaving of the PU traffic. They are the noise-only samples under the random arrival traffic, and the PU-signal-contained samples under the random leaving traffic. We propose the model in which the intra-frame sensing cooperates with the inter-frame one, and the inter-frame sensing uses the time-of-arrival (ToA), and time-of-leave (ToL) detectors to reduce the two degrading factors in the inter-frame sensing time. These ToA and ToL detectors are used to search for the sample which contains either the ToA or ToL of the PU traffic, respectively, which allows the partial cancelation of the unnecessary samples. At the final stage, the remaining samples are input into a primary user detector, which is based on the energy detection scheme, to determine the status of PU traffic in the inter-frame sensing time. The analysis and the simulation results show that the proposed scheme enhances the spectrum-sensing performance compared to the conventional counter-part.

• Journal of Environmental Health Sciences
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• v.9 no.2
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• pp.37-53
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• 1983

The contents of amino acids were examined in the 3, 4, 5, and 6 year-old roots of fresh ginseng and the 1979, 1980, 1981, and 1982 years' products of white and red ginsengs. Samples extracted with 75% ethanol for free amino acids and hydrolyzed with 6N-HCL for total amino acids were analyzed by Amino Acid Analyzer (Hitachi model KLA-5). The results were summarized as follows: 1. Amino acids from extracted samples were 18 kinds of Tryptophan, Lysine, Histidine, Arginine, Aspartic acid, Threonine, Serine, Glutamic acid, Proline, Glycine, Alanine, Cystine, Valine, Methionine, Isoleucine, Leucine, Tyrosine, and Phenylalanine. 2. Amino acids detected in hydrolyzed samples were 17 kinds execpt Tryptophan of extracted ones. 3. Arginine was the highest quantity of amino acids in ginseng. 4. The content of Tryptophan was 0.5690 mg/g in the 6 year-old fresh ginseng and trace quantities in other samples. 5. The contents of amino acids were increased in fresh ginseng according to cultivation year. 6. The contents of amino acids in white ginseng were slightly decreased but those in red ginseng were not changed during the storage time. 7. The content ratio of free amino acids to total amino acids were 1:3.

• Journal of Magnetics
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• v.14 no.4
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• pp.161-164
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• 2009

Iron-doped $Sn_{1-x}Fe_xO_2$ (x = 0.0, 0.05, 0.1, 0.2, 0.33) thin films on Si(100) substrates and powders were prepared by a chemical solution process. The x-ray diffraction (XRD) patterns of the $Sn_{1-x}Fe_xO_2$ thin films and powders showed a polycrystalline rutile tetragonal structure. Thermo gravimetric (TG) - differential thermal analysis (DTA) showed the final weight loss above $430{^{\circ}C}$ for all powder samples. According to XRD Rietveld refinement of the powders, the lattice parameters and unit cell volume decreased with increasing Fe content. The magnetic properties were characterized using a vibrating sample magnetometer (VSM) and M$\ddot{o}$ssbauer spectroscopy. The thin film samples with x = 0.1 and 0.2 showed paramagnetic properties but thin films with x = 0.33 exhibited ferromagnetic properties at room temperature. Mossbauer studies revealed the $Fe^{3+}$ valence state in the samples. The ferromagnetism in the samples can be interpreted in terms of the direct ferromagnetic coupling of ferric ions via an electron trapped in a bridging oxygen deficiency, which can be explained using the F-center exchange model.