• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1684-1690
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• 2004

This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1555-1560
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• 2018

$AlFe_2B_2$ produced by using a conventional arc melter has a ferromagnetic material with a Curie temperature ($T_C$) of around 300 K, but the arc-melt generates paramagnetic $Al_{13}Fe_4$ impurities during the synthesis of $AlFe_2B_2$. Impurities are brought to cause a decrease in magnetocaloric effects (MCEs). To investigate the effects of $Al_{13}Fe_4$ impurities on MCEs, we prepared and compared ascast and acid-treated samples, where the acid treatment was performed to remove the $Al_{13}Fe_4$ impurities. For the structural analysis, powder X-ray diffraction was carried out, and the measured data were subjected to a Rietveld refinement. The presence of $Al_{13}Fe_4$ impurities in the as-cast sample was observed in the phase analysis measurements. Magnetic properties were investigated by using Superconducting Quantum Interference Device (SQUID) measurements for the as-cast and the acid-treated $AlFe_2B_2$ samples. From isothermal magnetization measurements, Arrott plots were obtained showing that the transition of $AlFe_2B_2$ has a second-order magnetic phase transition (SOMT). The $T_C$ and the saturation magnetization increased for the acid-treated sample due to removal of the paramagnetic impurities. As a consequence, the magnetic entropy change ($-{\Delta}S$) increased in the pure $AlFe_2B_2$ samples, but the full width at half maximum in the plot of $-{\Delta}S$ vs. T decreased due to the absence of impurities.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.55.5-56
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• 2017

Galaxy dynamics probes weak gravity at accelerations below the de Sitter scale of acceleration adS = cH, where c is the velocity of light and H is the Hubble parameter. Low and high redshift galaxies hereby offer a novel probe of weak gravity in an evolving cosmology, satisfying H(z) = H0(1 + A(6z + 12z^2 +12z^3+ 6z^4+ (6/5)z^5)/(1 + z) with baryonic matter content A sans tension to H0 in surveys of the Local Universe. Galaxy rotation curves show anomalous galaxy dynamics in weak gravity aN < adS across a transition radius r beyond about 5 kpc for galaxy mass of 1e11 solar mass. where aN is the Newtonian acceleration based on baryonic matter content. We identify this behavior with a holographic origin of inertia from entanglement entropy, that introduces a C0 onset across aN=adS with asymptotic behavior described by a Milgrom parameter satisfying a0=omega/(2pi), where omega=sqrt(1-q)H is a fundamental eigenfrequency of the cosmological horizon. Extending an earlier confrontation with data covering 0.003 < aN/adS < 1 at redshift z about zero in Lellie et al. (2016), the modest anomalous behavior in the Genzel et al. sample at redshifts 0.854 < z <2.282 is found to be mostly due to clustering 0.36 < aN/adS < 1 close to the C0 onset to weak gravity and an increase of up to 65% in a0.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.55-61
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• 2007

Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximum distance, mean squared error, and maximum entropy sequential samplings. Through this research we team that the proposed approach is computationally inexpensive and provides good prediction performance.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.105-115
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• 2020

The study empirically examines the effects of loan portfolio diversification on bank risk and return in the nascent banking market of Vietnam. Loan portfolio diversification is captured through the Hirschman-Herfindahl index and the Shannon Entropy with sectoral exposures. We access each bank's financial reports to collect the required data, especially the breakdown of sectoral loan portfolios, thus constituting a unique dataset. To compute bank return, we use the traditional accounting indicators, including return-on-assets, return-on-equity, and net-interest margin. For bank risk, we utilize the loan-loss provisions and non-performing loans relative to gross customer loans. Using a sample of 30 commercial banks over the period from 2008 to 2019 and the system generalized method of moments estimator for the dynamic panel, we indicate the downsides of portfolio diversification. Concretely, we observe that all diversification measures exhibit significantly negative signs in all regressions across different bank return proxies. At the same time, the estimates display the significant and positive impact of diversification on the non-performing loan ratio. Hence, sectoral loan portfolio diversification significantly hampers bank performance in both aspects of lower return and higher credit risk. The results are robust across a rich set of bank performance and portfolio diversification measures.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1927-1943
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• 2016

Jitterbug is a passive network covert timing channel supplying reliable stealthy transmission. It is also the basic manner of some improved covert timing channels designed for higher undetectability. The existing entropy-based detection scheme based on training sample binning may suffer from model mismatching, which results in detection performance deterioration. In this paper, a new detection method based on the feature of Jitterbug covert channel traffic is proposed. A fixed binning strategy without training samples is used to obtain bins distribution feature. Coefficient of variation (CV) is calculated for several sets of selected bins and the weighted mean is used to calculate the final CV value to distinguish Jitterbug from normal traffic. Furthermore, the timing window parameter of Jitterbug is estimated based on the detected traffic. Experimental results show that the proposed detection method can achieve high detection performance even with interference of network jitter, and the parameter estimation method can provide accurate values after accumulating plenty of detected samples.

• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.291-301
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• 2016

One of the most concerned topics in ecology is the relationship between biodiversity and ecosystem functioning. However, there are few field studies, carried out in forests, although many studies have been done in controlled experiments in grasslands. In this paper, we describe the relationship pattern between three facets of diversity and productivity at Ratargul Fresh Water Swamp Forest (RFWSF) in Bangladesh, which is the only remaining fresh water swamp forest of the country. Sixty sample plots were selected from RFWSF and included six functional traits including leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), tree height, bark thickness and wood density. In analyzing TD, we used Shannon diversity and richness indices, functional diversity was measured by Rao's quadratic entropy (Rao 1982) and Faith's (1992) index was used for phylogenetic diversity (PD). It was found that, TD, FD and PD were positively related with productivity (basal area) due to resource use complementarity but surprisingly the best predictor of tree productivity was FD. The results contribute to the understanding the effects of biodiversity loss and it is essential for conservation decision-making and policy-making of Ratargul Fresh Water Swamp Forest.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3711-3716
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• 2021

The addition of ferric ferrocyanide (Prussian blue; PB) to adsorbents could enhance the adsorption performance of ¹³³Cs. Toward this goal, we present a heterogeneously integrated carbonaceous material platform consisting of PB in direct contact with CO₂-activated carbon filters (PB-CACF). The resulted sample retains 24.39% more PB than vice versa probed by the ultraviolet-visible spectrometer. We leverage this effect to capture ¹³³Cs in the aqueous environment via the increase in ionic strength and micropores. We note that the amount of PB was likely to be the key factor for ¹³³Cs adsorption compared with specific surface characteristics. The revealed adsorption capacity of PB-CACF was 21.69% higher than the bare support. The adsorption characteristics were feasible and spontaneous. Positive values of 𝜟H^o and 𝜟S^o show the endothermic nature and increased randomness. Based on the concept of capturing hazardous materials via hazardous materials, our work will be of interest within the relevant academia for collecting radionuclides in a sufficient manner.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.80-85
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• 2021

Radionuclides, particularly radioactive cesium (Cs), are a concern of human health in some nuclear power accidents. It could lead to a high level of intracellular accumulation due to its high radioactivity and long half-life. Therefore, it is imperative to develop a method to remove Cs from wastewater. Herein, we synthesized activated carbon fibers (ACFs) doped with Prussian blue (PB) via in situ methods. We classified samples by their preparation method as either physical (PB-ACF-A) or physicochemical (PB-ACF-B) syntheses for comparison. The PB-ACF-B sample showed a significant surface loss compared to PB-ACF-A but a better ¹³³Cs adsorption capacity. All samples fit well to Langmuir isotherms and the values of qmax were directly correlated to the amount of PB on the surface of the ACFs. Adsorption characteristics were further confirmed by the calculated free energy, enthalpy, and entropy.