• 한국환경보건학회지
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• 제22권3호
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• pp.49-55
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• 1996

Water parsley(Oenanthe javanica(Blume) DC) was raised with varying population density(S) in the laboratory aquarium unit to determine the growth equation. The population density was measure after 7 days. The resultant growth curve was well fit to the equation 1/S = A+B (1/S0) with a high correlation coefficient ($R^2$ = 0.999). The maximum specific absorption rate was $9.011 \times 10^{-5}$ kg $NO_x-N/kg$ water parsley$\cdot$day and $1.31 \times 10^{-5}$ kg $PO_4-P/kg$ water parsley$\cdot$day when the average population density was $2.62 kg/m^2$. The relationship between population density and nutrient absorption rate, the absorption rate of $NO_x-N$ was 5.04~5.24 mg/l$\cdot$day when the population density was $7.51~10.0 $mg/m^2\cdot day$ and the absorption rate of $PO_4-P$ was 0.56~0.78 mg/l$\cdot$day when the population density was 5.02~10.0 $kg/m^2\cdot day$. Taking into account the nutrient absorption rate and growth rate, the population density between $7.0 kg/m^2\cdot day$ and $8.0 kg/m^2 \cdot day$ was selected. The removal rate of nutrient was investigated after 7 days culture. Removal rate of $NO_x-N$ was 95.6~99.95% with initial concentration of 35 mg $NO_x-N/l$, and the removal rate of $PO_4-P$ was also high, indicating 80.24~98.9% with initial concentration of 5.95 mg $PO_x-P/l$.

• 한국컴퓨터정보학회논문지
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• 제20권8호
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• pp.29-33
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• 2015

This paper proposes a discrete logarithm algorithm that remarkably reduces the execution time of Pollard's Rho algorithm. Pollard's Rho algorithm computes congruence or collision of ${\alpha}^a{\beta}^b{\equiv}{\alpha}^A{\beta}^B$ (modp) from the initial value a = b = 0, only to derive ${\gamma}$ from $(a+b{\gamma})=(A+B{\gamma})$, ${\gamma}(B-b)=(a-A)$. The basic Pollard's Rho algorithm computes $x_i=(x_{i-1})^2,{\alpha}x_{i-1},{\beta}x_{i-1}$ given ${\alpha}^a{\beta}^b{\equiv}x$(modp), and the general algorithm computes $x_i=(x_{i-1})^2$, $Mx_{i-1}$, $Nx_{i-1}$ for randomly selected $M={\alpha}^m$, $N={\beta}^n$. This paper proposes 4-model Pollard Rho algorithm that seeks ${\beta}_{\gamma}={\alpha}^{\gamma},{\beta}_{\gamma}={\alpha}^{(p-1)/2+{\gamma}}$, and ${\beta}_{{\gamma}^{-1}}={\alpha}^{(p-1)-{\gamma}}$) from $m=n={\lceil}{\sqrt{n}{\rceil}$, (a,b) = (0,0), (1,1). The proposed algorithm has proven to improve the performance of the (0,0)-basic Pollard's Rho algorithm by 71.70%.

• 호남수학학술지
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• 제32권3호
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• pp.525-536
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• 2010

Let $X_1,X_2,\cdots$ be identically distributed $\rho$-mixing random variables with mean zeros and positive finite variances. In this paper, we prove $$\array{\lim\\{\in}\downarrow0}{\in}^2 \sum\limits_{n=3}^\infty\frac{1}{nlogn}P({\mid}S_n\mid\geq\in\sqrt{nloglogn}=1$$, $$\array{\lim\\{\in}\downarrow0}{\in}^2 \sum\limits_{n=3}^\infty\frac{1}{nlogn}P(M_n\geq\in\sqrt{nloglogn}=2 \sum\limits_{k=0}^\infty\frac{(-1)^k}{(2k+1)^2}$$ where $S_n=X_1+\cdots+X_n,\;M_n=max_{1{\leq}k{\leq}n}{\mid}S_k{\mid}$ and $\sigma^2=EX_1^2+ 2\sum\limits{^{\infty}_{i=2}}E(X_1,X_i)=1$.

• 자원환경지질
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• 제4권4호
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• pp.225-234
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• 1971

With the advance of civilization and steadily increasing population rivalry and competition for the use of the sewage, culverts, farm irrigation and control of various types of flood discharge have developed and will be come more and more keen in the future. The author has tried to calculated a formula that could adjust these conflicts and bring about proper solutions for many problems arising in connection with these conditions. The purpose of this study is to find out effective sewage, culvert, drainage, farm irrigation, flood discharge and other engineering needs in the Taegu area. If demands expand further a new formula will have to be calculated. For the above the author estimated methods of control for the probable expected rainfall using a formula based on data collected over a long period of time. The formula is determined on the basis of the maximum daily rainfall data from 1921 to 1971 in the Taegu area. 1. Iwai methods shows a highly significant correlation among the variations of Hazen, Thomas, Gumbel methods and logarithmic normal distribution. 2. This study obtained the following major formula: ${\log}(x-2.6)=0.241{\xi}+1.92049{\cdots}{\cdots}$(I.M) by using the relation $F(x)=\frac{1}{\sqrt{\pi}}{\int}_{-{\infty}}^{\xi}e^{-{\xi}^2}d{\xi}$. ${\xi}=a{\log}_{10}\(\frac{x+b}{x_0+b}\)$ ($-b<x<{\infty}$) ${\log}(x_0+b)=2.0448$ $\frac{1}{a}=\sqrt{\frac{2N}{N-1}}S_x=0.1954$. $b=\frac{1}{m}\sum\limits_{i=1}^{m}b_s=-2.6$ $S_x=\sqrt{\frac{1}{N}\sum\limits^N_{i=1}\{{\log}(x_i+b)\}^2-\{{\log}(x_0+b)\}^2}=0.169$ This formule may be advantageously applicable to the estimation of flood discharge, sewage, culverts and drainage in the Taegu area. Notation for general terms has been denoted by the following. Other notations for general terms was used as needed. $W_{(x)}$ : probability of occurranec, $W_{(x)}=\int_{x}^{\infty}f_{(n)}dx$ $S_{(x)}$ : probability of noneoccurrance. $S_{(x)}=\int_{-\infty}^{x}f_(x)dx=1-W_{(x)}$ T : Return period $T=\frac{1}{nW_{(x)}}$ or $T=\frac{1}{nS_{(x)}}$ $W_n$ : Hazen plot $W_n=\frac{2n-1}{2N}$ $F_n=1-W_x=1-\(\frac{2n-1}{2N}\)$ n : Number of observation (annual maximum series) P : Probability $P=\frac{N!}{{t!}(N-t)}F{_i}^{N-t}(1-F_i)^t$ $F_n$ : Thomas plot $F_n=\(1-\frac{n}{N+1}\)$ N : Total number of sample size $X_l$ : $X_s$ : maximum, minumum value of total number of sample size.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제21권2호
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• pp.113-120
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• 2014

In this paper, we first show that for any space X, there is a ${\sigma}$-complete Boolean subalgebra of $\mathcal{R}$(X) and that the subspace {${\alpha}{\mid}{\alpha}$ is a fixed ${\sigma}Z(X)^{\sharp}$-ultrafilter} of the Stone-space $S(Z({\Lambda}_X)^{\sharp})$ is the minimal basically disconnected cover of X. Using this, we will show that for any countably locally weakly Lindel$\ddot{o}$f space X, the set {$M{\mid}M$ is a ${\sigma}$-complete Boolean subalgebra of $\mathcal{R}$(X) containing $Z(X)^{\sharp}$ and $s_M^{-1}(X)$ is basically disconnected}, when partially ordered by inclusion, becomes a complete lattice.

• Journal of Magnetics
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• 제11권1호
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• pp.51-54
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• 2006

Variation of magnetic properties through Cr substitution for Co in inverse-spinel $CoFe_2O_4$ has been investigated by vibrating-sample magnetometry (VSM) and conversion electron $M\ddot{o}ssbauer$ spectroscopy (CEMS). $Cr_{x}Co_{1-x}Fe_2O_4$ samples were prepared as thin films by a sol-gel method. The lattice constant of the $Cr_{x}Co_{1-x}Fe_2O_4$ samples was found to remain unchanged, explainable in terms of a reduction of tetrahedral $Fe^{3+}$ ion to $Fe^{2+}$ due to substitution of $Cr^{3+}$ ion into octahedral $Co^{2+}$ site. The existence of the tetrahedral $Fe^{2+}$ ions in $Cr_{x}Co_{1-x}Fe_2O_4$ was confirmed by CEMS analysis. Room-temperature magnetic hysteresis curves for the $Cr_{x}Co_{1-x}Fe_2O_4$ films measured by VSM revealed that the saturation magnetization $M_s$ increases by Cr doping. The $M_s$ is maximized when x = 0.1 and decreases for higher x but is still bigger than that of $CoFe_2O_4$. The increase of $M_s$ can be explained partly by the reduction of the tetrahedral $Fe^{3+}$ ion to $Fe^{2+}$.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 추계학술대회
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• pp.99-100
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• 2022

자율운항 및 무인선 등 새로운 선박의 도입을 위한 기술개발과 함께 선박중심직접통신(M-S2X)의 필요성이 확대되고 있으며, 고용량, 고속의 서비스 구현을 위해 광대역 기반의 선박중심직접통신(Mx-S2X) 기술개발이 진행되고 있다. Mx-S2X의 안정적 서비스 구현을 위해 활용 가능한 주파수를 탐색하고 적정 주파수를 식별하고자 한다.

• 한국자기학회지
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• 제7권6호
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• pp.299-307
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• 1997

KrF 엑시머 Laser Ablation 법으로 (100)Si 기판위에 NdFeB 박막을 제조하였다. N $d_{x}$F $e_{90.98-x}$ $B_{9.02}$(x=17.51 ~ 27.51) 조성의 타겟을 사용하고, 레이저광에너지밀도를 2.75 ~ 5.99 J/c $m_{2}$, 기판온도를 620 ~ 700 .deg. C로 하여 제공정 조건이 박막의 자기특성, 결정배향성 및 미세조직에 미치는 영향을 조사한 결과, 증착상태의 시편에서 우선결정배향성은 관찰되지 않았으나 4 .pi. $M_{s}$ .approximately equal. 7 kG, 4 .pi. $M_{r}$ .approximately equal. 4 kG 및 $H_{c}$ = 300 ~ 1000 Oe인 자기특성이 얻어졌다. 증착 속도는 에너지밀도의 증가에 따라 직선적으로 증가 하였으며, 기판온도 증가에 따라서는 현저한 변화가 관찰되지 않았다. 3 J/c $m_{2}$의 에너지밀도가 최적의 조건이었으며, Nd량의 증가에 따라 4 .pi. $M_{s}$ 의 현저한 감소없이 4 .pi. $M_{r}$ 과 $H_{c}$가 증가하는 경향이 확인되었다.되었다.다.

• Elastomers and Composites
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• 제28권2호
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• pp.103-107
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• 1993

To make new filler for conducting rubber, the sample of perovskite-related ferrite system $Dy_{2-x}Sr_{1+x}Fe_2O_{7-y}$ (x=0.0, 0.5, 1.0, 1.5, and 2.0) were synthesized at 1473K in air. $M{\ddot{o}}ssbauer$ spetrum of x=0.0 sample shows typical six line pattern with $M{\ddot{o}}ssbauer$ parameters, $I.S=3.6{\times}10^{-1}mm/sec,\;E_Q=-7.0{\times}10^{-2}mm/sec,\;H_{int}=5.19{\times}10^2\;Koe$. In case of x=2.0, the spectrum is composed of single line exhibiting coexistance of $Fe^{3+}(I.S.=3.7{\times}10^{-1}mm/sec)$ ions and $Fe^{4+}(I.S.=-1.9{\times}10^{-1}mm/sec)$ ions. With increase in x value electrical conductivity at constant temperature sharply increased and the activation energies decreased from $3.8{\times}10^{-1}\;to\;1.9{\times}10^{-1}\;eV$.

• Kyungpook Mathematical Journal
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• 제61권4호
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• pp.679-710
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• 2021

We study the tensor product algebra P_k(x₁) ⊗ P_k(x₂) ⊗ ⋯ ⊗ P_k(x_m), where P_k(x) is the partition algebra defined by Jones and Martin. We discuss the centralizer of this algebra and corresponding Schur-Weyl dualities and also index the inequivalent irreducible representations of the algebra P_k(x₁) ⊗ P_k(x₂) ⊗ ⋯ ⊗ P_k(x_m) and compute their dimensions in the semisimple case. In addition, we describe the Bratteli diagrams and branching rules. Along with that, we have also constructed the RS correspondence for the tensor product of m-partition algebras which gives the bijection between the set of tensor product of m-partition diagram of P_k(n₁) ⊗ P_k(n₂) ⊗ ⋯ ⊗ P_k(n_m) and the pairs of m-vacillating tableaux of shape [λ] ∈ Γ_k^m, Γ_k^m = {[λ] = (λ₁, λ₂, …, λ_m)|λ_i ∈ Γ_k, i ∈ {1, 2, …, m}} where Γ_k = {λ_i ⊢ t|0 ≤ t ≤ k}. Also, we provide proof of the identity $(n_1n_2{\cdots}n_m)^k={\sum}_{[{\lambda}]{\in}{\Lambda}^k_{{n_1},{n_2},{\ldots},{n_m}}}$ f^[λ]m_k^[λ] where m_k^[λ] is the multiplicity of the irreducible representation of $S{_{n_1}}{\times}S{_{n_2}}{\times}....{\times}S{_{n_m}}$ module indexed by ${[{\lambda}]{\in}{\Lambda}^k_{{n_1},{n_2},{\ldots},{n_m}}}$, where f^[λ] is the degree of the corresponding representation indexed by ${[{\lambda}]{\in}{\Lambda}^k_{{n_1},{n_2},{\ldots},{n_m}}}$ and ${[{\lambda}]{\in}{\Lambda}^k_{{n_1},{n_2},{\ldots},{n_m}}}=\{[{\lambda}]=({\lambda}_1,{\lambda}_2,{\ldots},{\lambda}_m){\mid}{\lambda}_i{\in}{\Lambda}^k_{n_i},i{\in}\{1,2,{\ldots},m\}\}$ where ${\Lambda}^k_{n_i}=\{{\mu}=({\mu}_1,{\mu}_2,{\ldots},{\mu}_t){\vdash}n_i{\mid}n_i-{\mu}_1{\leq}k\}$.