• Honam Mathematical Journal
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• v.38 no.2
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• pp.243-257
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• 2016

If we let $L(2K;n):=\sum_{s=0}^{k-1}(^{2k}_{2s+1})\sum_{m=1}^{n-1}{\sigma}_{2k-2s-1,1}(m;2){\sigma}_{2s+1,1}(n-m;2)$ with $${\sigma}_{k,l}(n;2):=\sum\limits_{{d{\mid}n}\atop{d{\equiv}l(mod2)}}d^k$$, then we get the formula of L(2u; p)L(2v; p)L(2w; p).

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.188-191
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• 2004

We experimented variables of four kinds(a/d=1.5, 2.5, 3.5, 4.5) of shear span ratio to consider a structural characteristic of high-strength lightweight concrete beam used industrial by-product. Through the research of serials, the more increase of shear span ratio, the more ductility is superior. Rating the capacity of high-strength concrete beam and the capacity of lightweight concrete beam, in existing lightweight concrete beam evaluation formula, if a shear strength formula for normal concrete multiplies 0.85(reduction factor), it is rated as safety side over shear span ratio 2.5, but it is riskful at low shear span ratio. Therefore it is important that these factors are considered as the evaluation.

• Architectural research
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• v.20 no.3
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• pp.75-82
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• 2018

The aim of the study is to evaluate the concrete shear strength and structural behavior of two general beams and eight shape memory alloys (SMAs)-reinforced beams under the flexural test. This work compares the existing reference formula for concrete shear strength with test result to provide the basic data for the design of highly intelligent reinforced concrete (hereinafter, HIRC) beams. The evaluation of the concrete shear strength was performed with effective depth (d=65, 70, 80), SMA diameter change (ø=2.0, 2.5) as the main variables of the specimens. For the relationship between the effective depth and the $V_{\exp}/V_{cal}$, the test result shows that the concrete shear strength gradually approaches 1.0 as the effective depth length increase. For the AIJ formula, the specimens are approached evenly for comparison between $V_{\exp}/V_{cal}$ and the by-product (garnet, fly-ash) reinforced specimen; however, other formulas indicate a deviation.

• The Pure and Applied Mathematics
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• v.12 no.4 s.30
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• pp.275-287
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• 2005

Geometric invariants are basic tools for geometric processing and computer vision. In this paper, we give a linear approximation for the differentiation of the binormal vector field of a space curve by using the forward and backward differences of discrete binormal vectors. Two kind of discrete torsion, say, back-ward torsion $T_b$ and forward torsion $T_f$ can be defined by the dot product of the (backward and forward) discrete differentiation of binormal vectors that are linear approximations of torsion. Using Frenet formula and Taylor series expansion, we give error estimations for the discrete torsions. We also give numerical tests for a curve. Notably the average of $T_b$ and $T_f$ looks more stable in errors.

• Honam Mathematical Journal
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• v.41 no.1
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• pp.163-174
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• 2019

The veritable pursuit of this exegesis is to exhibit integrals affined with the Bessel-Struve kernel function, which are explicitly inscribed in terms of generalized (Wright) hypergeometric function and also the product of generalized (Wright) hypergeometric function with sum of two confluent hypergeometric functions. Somewhat integrals involving exponential functions, modified Bessel functions and Struve functions of order zero and one are also obtained as special cases of our chief results.

• East Asian mathematical journal
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• v.25 no.4
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• pp.481-486
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• 2009

The first object of this note is to show that a summation formula due to Padmanabham for three-variables hypergeometric function $X_8$ introduced by Exton can be proved in a different (from Padmanabham's and his observation) yet, in a sense, conventional method, which has been employed in obtaining a variety of identities associated with hypergeometric series. The second purpose is to point out that one of two seemingly new hypergeometric identities due to Exton was already recorded and the other one is easily derivable from the first one. A corrected and a little more compact form of a general transform involving hypergeometric functions due to Exton is also given.

• Korean Journal of Food Science and Technology
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• v.37 no.4
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• pp.648-655
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• 2005

Effects of two types of formula designed for commercial product on forced-swimming capacity and related biochemical parameters of Sprague-Dawley rats were evaluated. Treatment groups were fed diet supplemented with 4.56% formula A (EFA: exercise-trained and formula A-supplemented group) or B (EFB: exercise-trained and formula B-supplemented group) for 4 weeks. All groups were regularly trained twice a week to be accustomed to swimming. After 4 weeks, each group was divided into two subgroups: 90 min swimming (90-min subgroups) and swimming until exhaustion (all-out subgroups). Serum was collected, and skeletal muscles were dissected out after swimming. Forced-swimming time until exhaustion was significantly longer (p<0.05) in EFA and EFB groups than that of exercised control group. Among 90-min subgroups, serum ammonia level of EFB group was significantly lower than that of exercised control group (p<0.05). Among all-out subgroups, serum lactate levels were not significantly different among exercised control and treatment groups, whereas serum-free fatty acid levels of EFA and EFB groups were significantly lower than that of exercised control group (p<0.05). In conclusion, EFA and EFB groups enhanced forced swimming capacity of rats by increasing fat utilization and delaying accumulation of serum fatigue factor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.89-93
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• 2007

In this study, we consider an assembly line operated under a base-stock policy. A product consists of two parts, and a finished product transfers to a warehouse in which demands are satisfied. Assume that demands arrive according to a Poisson process and processing times at each production line are exponentially distributed. Whenever a demand arrives, it is satisfied immediately from an inventory in the warehouse if available; otherwise, it is backlogged and satisfied later by the next product exiting from production lines. In either case, an arriving demand automatically triggers the production of a part at both production lines. These two parts will be assembled into a product that eventually transfers to the warehouse. We obtain a closed form formula of approximation for delay time or lead time distribution of a demand when a base- stock level is s. Moreover, it can be applied to the optimal base-stock level which minimizes the total inventory cost. Numerical examples are presented to show our optimal base-stock level's quality.

• Honam Mathematical Journal
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• v.35 no.2
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• pp.179-200
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• 2013

Let $C[0,t]$ denote the function space of all real-valued continuous paths on $[0,t]$. Define $X_n:C[0,t]{\rightarrow}\mathbb{R}^{n+1}$ by $Xn(x)=(x(t_0),x(t_1),{\cdots},x(t_n))$, where $0=t_0$ < $t_1$ < ${\cdots}$ < $t_n$ < $t$ is a partition of $[0,t]$. In the present paper, using a simple formula for the conditional expectation given the conditioning function $X_n$, we evaluate the $L_p(1{\leq}p{\leq}{\infty})$-analytic conditional Fourier-Feynman transform and the conditional convolution product of the cylinder functions which have the form $$f((v_1,x),{\cdots},(v_r,x))\;for\;x{\in}C[0,t]$$, where {$v_1,{\cdots},v_r$} is an orthonormal subset of $L_2[0,t]$ and $f{\in}L_p(\mathbb{R}^r)$. We then investigate several relationships between the conditional Fourier-Feynman transform and the conditional convolution product of the cylinder functions.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.289-294
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• 2020

In order to control the quality of X-ray images and patient exposure, it is necessary to document the output dose(air absorption dose(mGy)) output from the X-ray unit from the measurement. The purpose of this study is to find an equation that can calculate the output dose from the measurement of the output dose and output factor(Of) of the X-ray Unit. The output dose and output factors of the X-beam irradiated from the X-ray unit were measured using an XR multi-detector. The output dose calculation formula was obtained by fitting the measured output dose divided by the tube current-exposure time product(mAs) and the set tube voltage with Allometric1. The final output dose calculation formula was obtained by multiplying this formula with the output factor. It is considered that the obtained final output dose calculation formula will be useful for all tube voltages, tube currents, exposure times, field sizes, and distances.