• Journal of applied mathematics & informatics
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• 제37권3_4호
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• pp.177-182
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• 2019

This study is about the number expressed and the number not expressed in terms of the sum of consecutive natural numbers with a difference of 2k. Since it is difficult to generalize in cases of onsecutive positive integers with a difference of 2k, the table of cases of 4, 6, 8, 10, and 12 was examined to find the normality and to prove the hypothesis through the results. Generalized guesswork through tables was made to establish and prove the hypothesis of the number of possible and impossible numbers that are to all consecutive natural numbers with a difference of 2k. Finally, it was possible to verify the possibility and impossibility of the sum of consecutive cases of 124 and 2010. It is expected to be investigated the sum of consecutive natural numbers with a difference of 2k + 1, as a future research task.

• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.37-51
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• 2003

Enumeration of the primes with difference 4 between consecutive primes, is counted up to 5${\times}$10$\^$10/, yielding the counting function ,r2,4(5${\times}$10$\^$10/) = l18905303. The sum of reciprocals of primes with gap 4 between consecutive primes is computed B$_4$(5 ${\times}$ 10$\^$10/) = 1.1970s4473029 and B$_4$ = 1.197054 ${\pm}$ 7 ${\times}$ 10$\^$-6/. And Enumeration of the primes with difference 6 between consecutive primes, is counted up to 5${\times}$10$\^$10/, yielding the counting function $\pi$$\_$2.6/(5${\times}$10$\^$10/) = 215868063. The sum of reciprocals of primes with gap 6 between consecutive primes is computed B$\_$6/(5${\times}$10$\^$10/) = 0.93087506039231 and B$\_$6/ = 1.135835 ${\pm}$ 1.2${\times}$10$\^$-6/.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.441-452
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• 2006

We introduce the counting function ${\pi}^*_{2.8}(x)$ of the primes with difference 8 between consecutive primes ($p_n,\;p_{n+l}=p_n+8$) can be approximated by logarithm integral $Li^*_{2.8}$. We calculate the values of ${\pi}^*_{2.8}(x)$ and the sum $C_{2,8}(x)$ of reciprocals of primes with difference 8 between consecutive primes $p_n,\;p_{n+l}=p_n+8$ where x is counted up to $7{\times}10^{10}$. From the results of these calculations. we obtain ${\pi}^*_{2.8}(7{\times}10^{10}$)= 133295081 and $C_{2.8}(7{\times}10^{10}) = 0.3374{\pm}2.6{\times}10^{-4}$.

• 한국수학사학회지
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• 제19권1호
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• pp.1-16
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• 2006

수학에서 가장 매력적이고 중요한 이론들 중에 하나로 알려진 베르누이 (Bernoulli)수의 변천과정을 고찰한다. 즉, 당시대의 이러한 연속된 정수의 멱의 합에 대한 수학사적 배경들을 조사하고, 베르누이 수와 관련된 연구들이 현재 어떠한 방향으로 진행되고 있는지를 살펴본다.

• 사물인터넷융복합논문지
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• 제6권4호
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• pp.49-57
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• 2020

본 논문은 연속하는 두 소수의 차가 10인 소수의 쌍의 수에 대한 계산 함수 π*2,10(x)의 근사함수 Li*2,10(x)를 로그적분을 이용하여 유도하였다. Li*2,10(x)가 π*2,10(x)의 근사함수로 적절한지 알아보기 위하여 컴퓨터와 Mathematica 프로그램을 이용하여 π*2,10(x)와 Li*2,10(x)의 값을 x ≤ 1011까지 구한 후 두 값의 오차율을 계산하였다. 오차율을 계산한 결과 대부분의 구간에서 오차율이 0.005% 이하로 나타났다. 또한, 두 소수의 차가 10인 소수들의 역수들의 합 C2,10(∞)이 유한임을 보였다. C2,10(∞)의 수렴값을 구하기 위하여 C2,10(1011)을 구한 후, 이를 이용하여 C2,10(∞)의 대략적인 수렴값을 계산하였다. 그 결과 C2,10(∞)=0.4176±2.1×10-3로 수렴함을 알 수 있었다.

• 한국수학사학회지
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• 제20권4호
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• pp.71-84
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• 2007

베르누이가 처음으로 자연수 k에 대하여 합 $S_n(k)=\sum_{{\iota}=1}^n\;{\iota}^k$에 관한 공식들을 유도하는 방법을 발견하였다([4]). 그 이후, 리만 제타함수와 관련된 베르누이 수와 오일러 수에 관한 성질들이 연구되어왔다. 최근에 김태균은 $\mathbb{Z}_p$상에서 p-진 q-적분과 관련된 확장된 q-베르누이 수와 q-오일러 수, 연속된 q-정수의 멱수의 합에 관한 성질들을 밝혔다. 본 논문에서는 연속된 q-정수의 멱수의 합에 관한 역사적 배경과 발달과정을 고찰하고, 오일러 및 베르누이 수와 관련된 리만 제타함수가 해석적 함수로써 값을 가지는 문제를 q-확장된 부분의 이론으로 연구되어온 q-오일러 제타함수에 대해 체계적으로 논의한다.

• 전기학회논문지
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• 제66권12호
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• 한국환경보건학회:학술대회논문집
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• 한국환경보건학회 2004년도 International Conference Current Challenges and Advances in Environmental Health
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• pp.45-50
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• 2004

Indoor air quality can be affected by indoor sources, ventilation, decay and outdoor levels. Alt hough technologies exist to measure these factors, direct measurements are often difficult. The purpose of this study was to develop an alternative method to characterize indoor environmental factors by multiple indoor and outdoor measurements. Daily indoor and outdoor NO2 concentrations were measured for 30 consecutive days in 28 houses in Brisbane, Australia, and for 21 consecutive days in 37 houses in Seoul, Korea. Using a mass balance model and regression analysis, penetration factor (ventilation rate divided by the sum of ventilation rate and deposition constant) and source strength factor (source strength divided by the sum of ventilation rate and deposition constant) were calculated using multiple indoor and outdoor measurements. Subsequently, the ventilation rate and NO2 source strength were estimated. Geometric means of ventilation rate were 1.44 ACH in Brisbane, assuming a residential NO2 deposition constant of 1.05 hr-1, and 1.36 ACH in Seoul, with the measured residential NO2 deposition constant of 0.94 hr-1. Source strengths of N02 were 15.8 ${\pm}$ 18.2 ${\mu}$g/m3${\cdot}$hr and 44.7 ${\pm}$ 38.1 ${\mu}$g/m3${\cdot}$hr in Brisbane and Seoul, respectively. In conclusion, indoor environmental factors were effectively characterized by this method using multiple indoor and outdoor measurements.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 봄 학술발표회 발표논문집
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• pp.155-160
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• 2003

Indoor air quality can be affected by indoor sources, ventilation, decay and outdoor levels. Although technologies exist to measure these factors, direct measurements are often difficult. The purpose of this study was to develop an alternative method to characterize indoor environmental factors by multiple indoor and outdoor measurements. Daily indoor and outdoor $NO_2$ concentrations were measured for 30 consecutive days in 28 houses in Brisbane, Australia, and for 21 consecutive days in 37 houses in Seoul, Korea. Using a mass balance model and regression analysis, penetration factor (ventilation rate divided by the sum of ventilation rate and deposition constant) and source strength factor (source strength divided by the sum of ventilation rate and deposition constant) were calculated using multiple indoor and outdoor measurements. Subsequently, the ventilation rate and $NO_2$ source strength were estimated. Geometric means of ventilation rate were 1.44 ACH in Brisbane, assuming a residential $NO_2$ deposition constant of 1.05 $hr^{-1}$, and 1.36 ACH in Seoul, with the measured residential $NO_2$ deposition constant of 0.94 $hr^{-1}$. Source strengths of $NO_2$ were 15.8 $\pm$ 18.2 ${\mu}g$/$m^3$.hr and 44.7 $\pm$ 38.1${\mu}g$/$m^3$.hr in Brisbane and Seoul, respectively. In conclusion, indoor environmental factors were effectively characterized by this method using multiple indoor and outdoor measurements.

• 품질경영학회지
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• 제33권3호
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• pp.126-134
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• 2005

This paper proposes a selectively cumulative sum(S-CUSUM) control chart for detecting shifts in the process mean. The basic idea of the S-CUSUM chart is to accumulate previous samples selectively in order to increase the sensitivity. The S-CUSUM chart employs a threshold limit to determine whether to accumulate previous samples or not. Consecutive samples with control statistics out of the threshold limit are to be accumulated to calculate a standardized control statistic. If the control statistic falls within the threshold limit, only the next sample is to be used. During the whole sampling process, the S-CUSUM chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L -consecutive control statistics fall outside the threshold limit. The number L is a decision variable and is called a 'control length'. A Markov chain approach is employed to describe the S-CUSUM sampling process. Formulae for the steady state probabilities and the Average Run Length(ARL) during an in-control state are derived in closed forms. Some properties useful for designing statistical parameters are also derived and a statistical design procedure for the S-CUSUM chart is proposed. Comparative studies show that the proposed S-CUSUM chart is uniformly superior to the CUSUM chart or the Exponentially Weighted Moving Average(EWMA) chart with respect to the ARL performance.