• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.5
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• pp.778-781
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• 2016

Sea urchin skeleton (SUS) generated from production of sea urchin eggs was used as a biodegradable film base material, and its composite film with gelatin was prepared. In addition, Robinia pseudoacacia flower extract (RFE) was incorporated into the film-forming solution to provide antioxidant and anti-microbial activities. The tensile strength (TS) of the SUS/gelatin composite films increased with increasing gelatin content, whereas elongation at break (E) decreased. Among the composite films, SUS/gelatin film at a ratio of 8:2 (w/w) exhibited the most desirable TS and E values. Furthermore, SUS composite film containing RFE showed a reduced TS and increased E compared to the control film. Based on 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) and 2,2-diphenyl-1-picrylhydrazyl radical scavenging assays and disc diffusion results against growth of Listeria monocytogenes, antioxidant and anti-microbial activities of films increased with increasing RFE concentration. Consequently, SUS composite film containing RFE showed proper physical properties as well as antioxidant and anti-microbial activities. These results indicate that SUS can be used as a film base material while the SUS composite film containing RFE can be utilized as active packaging.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.384-393
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• 2015

This paper proposes an optimized classifier based on feature elimination (OCFE) for gene selection with combining two feature elimination methods, ReliefF and SVM-RFE. ReliefF algorithm is filter feature selection which rank the data by the importance of the data. SVM-RFE algorithm is a wrapper feature selection which wrapped the data and rank the data based on the weight of feature. With combining these two methods we get less error rate average, 0.3016138 for OCFE and 0.3096779 for SVM-RFE. The proposed method also get better accuracy with 70% for OCFE and 69% for SVM-RFE.

• Korean Journal of Plant Taxonomy
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• v.37 no.4
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• pp.363-386
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• 2007

Endemic element of the Russian Far East (RFE) flora includes 497 species of 150 genera and 46 families. The level of endemism in structure of regional natural flora reaches 11.1% as a whole, and in structure of its native fraction - 13.1%. As a result of chorologic and ecocenotic analysis of RFE flora endemic element it is revealed that it consists of 8 main geographical groups and 7 main floristic complexes. The largest number of endemic species is concentrated in Arctic - Alpine & Montane (140, 28.2%), Forest (107, 21.5%) and Maritime (88, 17.7%) floristic complexes as well as in Russian Far East - West-Pacific (136, 27.4%), Japan Sea (88, 17.7%) and North-East-Asian - Beringian (69, 13.9%) geographical groups. It's possible to distinguish three main areas with similar eco-geographical differentiation of endemics on RFE as follows: (1) North-East Asia sector of RFE which North-East-Asian - Beringian and Maritime Okhotia - Beringian geographical groups approximately correspond to, (2) Continental part of East Asia sector of RFE (West - Okhotian, Amur - Okhotian, Amur - Ussirian, Okhotsk Sea and Japan Sea groups) and (3) Oceanic part of East Asia sector (Russian Far East - West Pacific group). Taxonomical variety of RFE endemics on these territories makes up accordingly (1) - 99 species (19.9%), (2) - 259 (52.8%) and (3) - 136 (27.4%).

• Korean Journal of Materials Research
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• v.5 no.8
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• pp.913-920
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• 1995

RFe$_2$(R=rare earth) Laves Phase intermetallic compounds are one of the promising materials for magnetostrictive applications, due to large magnetostriction coefficients in the order of 10$^{-3}$ . However, because RFe$_2$intermetallic compounds have large magnetostriction constants as well as large magnetocrystalline anisotropy constants, a large external magnetic field is necessary to reach saturation magnetostriction. Hence researches on giant magnetostriction have been concentrated on producing materials exhibiting a high value of magnetostriction in a low magentic field. The main research trend of the giant magnetostriction to obtain the large value in the low magnetic filed, fortunately as the signs of magnetocrystalline anisotropy constans in RFe$_2$intermetallic compounds alternate with the rare earth metals, has been to substitute the rare earth metal for others and hence to reduce the magnetocrystalline anisotropy energy. In addition, amorphous RFe$_2$alloys have been researched. In this research, both of the methods which are substitution of the rare earth metal and amorphization in RFe$_2$ intermetallic compounds are simultaneously conducted to obtain the large magnetostriction coefficient in the low external magnetic field. Among them, SmFe$_2$and PrFe$_2$are selected, and amorphized in substrate-free bulk state. Magnetism in amorphous bulk (Sm$_{1-x}$ Pr$_{x}$) Fe$_2$alloys is investigated in the low magnetic field.ld.

• Journal of Magnetics
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• v.8 no.4
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• pp.153-156
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• 2003

The values of crystalline-electric-field parameters $A_{nm}$ for $RFe_{11}$Ti $H_{x}$ (R=Tb,Ho) (x=0,l) are obtained by fitting calculations to the magnetization curves along the crystal axes at 4.2 K and higher temperatures. The insertion of H element in RFe$_{11}$Ti significantly affects CEF parameters $A_{nm}$ . By using exchange field 2${\mu}$$_{B}$ $H_{ex}$ derived by inelastic neutron scattering and fitted $A_{nm}$ , the calculations reproduce the experimental curves well.

• Proceedings of the Korean Magnestics Society Conference
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• 2003.12a
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• pp.288-288
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• 2003

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of Magnetics
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• v.1 no.2
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• pp.64-68
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• 1996

Compared with the conventional magnetostriction in Ni alloys which are in the order of several tens ppm (Parts Per Million =10-6), RFe$_2$(R = rare earth element) Laves Phase intermetallic compounds show large saturation magnetostriction in the range of a few thousands ppm. However, the large external magnetic field necessary to obtain saturatio magnetostriction has due to large magnetocrystalline anisotropy energy restrained the applicationof magnetostriction materials in RFe$_2$intermetallic compounds. As a result of its solution, the largest published value of effective giant magnetostriction in a low external magnetic field (less than a few hundred Oe) is reported in this paper by means of amorphisation of RFe$_2$intermetallic compounds with the addition of boron, as a half metal. For the amorphous (SmFe$_2$)0.97 B0.03 alloys, the effective magnetostriction of -545 and -610 $\times$ 10-6 is obtained at 400 and 1,000 Ie, respectively. Moreover, the effective magnetostriction of 590 and 630$\times$10-6 in the amorphous (TbFe$_2$)0.98 B0.02 alloys is also found at 400 and 1,000 Oe, respectively. This result will provide a clue to understanding the effect of half metal on anomalous increase of the effective giant magnetostriction and attract the great attention for magnetostriction applications.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.675-681
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• 2016

A 41dB gain control range $6^{th}$-order band-pass receiver front-end (RFE) using CMOS switched frequency translated impedance (FTI) is presented in a 40 nm CMOS technology. The RFE consists of a frequency tunable RF band-pass filter (BPF), IQ gm cells, and IQ TIAs. The RF BPF has wide gain control range preserving constant filter Q and pass band flatness due to proposed pre-distortion scheme. Also, the RF filter using CMOS switches in FTI blocks shows low clock leakage to signal nodes, and results in low common mode noise and stable operation. The baseband IQ signals are generated by combining baseband Gm cells which receives 8-phase signal outputs down-converted at last stage of FTIs in the RF BPF. The measured results of the RFE show 36.4 dB gain and 6.3 dB NF at maximum gain mode. The pass-band IIP3 and out-band IIP3@20 MHz offset are -10 dBm and +12.6 dBm at maximum gain mode, and +14 dBm and +20.5 dBm at minimum gain mode, respectively. With a 1.2 V power supply, the current consumption of the overall RFE is 40 mA at 500 MHz carrier frequency.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.