• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10a
• /
• pp.18-20
• /
• 1999

히스토그램은 데이터베이스 질의 최적기가 사용하는 통게정보 중의 하나이다. 최근에는 데이터베이스의 크기가 기하급수적으로 커짐에 따라, 데이터의 전체적인 성향을 빠르게 파악할 수 있는 방법의 하나로 히스토그램으로 활용하는 방안이 고려되고 있다. 그를 위해서, 히스토그램에서 얻어진 근사값의 오차를 추정할 수 있는 방법이 요구되었다. 기존의 기법에서는 히스토그램의 각 버켓에 실제 빈도와 평균 빈도의 최대차를 추가하고, 이 값을 이용하여 오차추정을 하였다. 그러나, 이 값이 히스토그램 버켓의 전체적인 데이터 분포를 잘 반영하지 못하기 때문에 실제 오차에 근접한 오차 추정을 할 수가 없는 단점이 있었다. 본 논문에서는 이를 극복하기 위해, 히스토그램에 데이터의 분포를 잘 반영하는 정보 즉, 평균값, COUNT/SUM 연산에 대한 최대 오차를 추가하였다. 이 정보들을 이용하여 실제 오차에 보다 근접한 오차 추정을 할 수 있었으며, 부가적으로 SUM/AVG 연산에 대한 보다 정확한 근사값을 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.211-213
• /
• 2001

히스토그램은 질의 최적화글 위해 사용되는 튿-계 정또 중 하나이다. 최근에는 방대한 데이타에 대한 범위 질의의 선택도 추정 방법의 하나로 사용되기도 한다. 히스토그램을 통한 범위 질의의 선택도 추정 결과는 항상 오차를 포함한다. 따라서 결과의 신뢰성을 보장하기 위해 선택도에 대한 오차를 추정하는 방법이 요구된다. 추정된 선택도의 오차 추정에 대한 기존 방법은 1차원 히스토그램만을 고려하여 하나의 애트리뷰트의 값에 따라 빈도의 분포를 반영하므로 애트리뷰트가 많은 다차원 히스토그램에 바로 적용시키는데 문제가 있다. 이 논문에서는 기존의 추정된 선택도에 대한 오차 추정 기법들을 다차원에 적용할 수 있게 확장한 M-Max, M-Sum 기법을 제안하고, 두 기법을 합친 하이브리드 기법을 제안한다. 실험을 통해 M-Sum 기법과 하이브리드 기법이 M-Max 기법보다 정확한 오차 추정 기법임을 보이고, 또한 작은 기억 공간에서도 두 기법이 오차를 보다 정확하게 추정함을 보인다.

• Journal of the Korean Mathematical Society
• /
• v.56 no.6
• /
• pp.1599-1611
• /
• 2019

Let $m,n{\in}{\mathbb{N}}$. We represent the additive subgroups of the ring ${\mathbb{Z}}_m{\times}{\mathbb{Z}}_n$, which are also (unital) subrings, and deduce explicit formulas for $N^{(s)}(m,n)$ and $N^{(us)}(m,n)$, denoting the number of subrings of the ring ${\mathbb{Z}}_m{\times}{\mathbb{Z}}_n$ and its unital subrings, respectively. We show that the functions $(m,n){\mapsto}N^{u,s}(m,n)$ and $(m,n){\mapsto}N^{(us)}(m,n)$ are multiplicative, viewed as functions of two variables, and their Dirichlet series can be expressed in terms of the Riemann zeta function. We also establish an asymptotic formula for the sum $\sum_{m,n{\leq}x}N^{(s)}(m,n)$, the error term of which is closely related to the Dirichlet divisor problem.

• Communications for Statistical Applications and Methods
• /
• v.26 no.2
• /
• pp.175-189
• /
• 2019

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.3
• /
• pp.159-168
• /
• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4A
• /
• pp.414-422
• /
• 2011

This paper describes a design of low-density parity-check(LDPC) decoder supporting block length 2,304-bit and code rate 1/2 of IEEE 802.16e mobile WiMAX standard. The designed LDPC decoder employs the min-sum algorithm and partially parallel layered-decoding architecture which processes a sub-matrix of $96{\times}96$ in parallel. By exploiting the properties of the min-sum algorithm, a new memory reduction technique is proposed, which reduces check node memory by 46% compared to conventional method. Functional verification results show that it has average bit-error-rate(BER) of $4.34{\times}10^{-5}$ for AWGN channel with Fb/No=2.1dB. Our LDPC decoder synthesized with a $0.18{\mu}m$ CMOS cell library has 174,181 gates and 52,992 bits memory, and the estimated throughput is about 417 Mbps at 100-MHz@l.8-V.

• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.2
• /
• pp.53-59
• /
• 2018

This paper proposes a pseudo-random beamforming technique for time-synchronized mobile base stations (BSs) for multi-cell downlink networks which have mobility. The base stations equipped with multi-antennas and mobile stations (MSs) are time-synchronized based on global positioning system (GPS) signals and generate a number of transmit beamforming matrix candidates according to the predetermined pseudo-random pattern. In addition, MSs generate receive beamforming vectors that correspond to the beam index number based on the minimum mean square error (MMSE) using transmit beamforming vectors that make up a number of transmit beamforming matrices and wireless channel matrices from BSs estimated via the reference signals (RS). Afterward, values of received signal-to-interference-plus-noise ratio (SINR) with regard to all transmit beamforming vectors are calculated, and the resulting values are then feedbacked to the BS of the same cells along with the beam index number. Each of the BSs calculates each of the sum-rates of the transmit beamforming matrix candidates based on the feedback information and then transmits the calculated results to the BS coordinator. After this, optimum transmit beamforming matrices, which can maximize a sum-rate of the entire cells, are selected at the BS coordinator and informed to the BSs. Finally, data signals are transmitted using them. The simulation results verified that a sum-rate of the entire cells was improved as the number of transmit beamforming matrix candidates increased. It was also found that if the received SINR values and beam index numbers are feedbacked opportunistically from each of the MSs to the BSs, not only nearly the same performance in sum-rate with that of applying existing feedback techniques could be achieved but also an amount of feedback was significantly reduced.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.171-176
• /
• 2019

Drones are highly utilized because they can efficiently acquire long-distance videos. In drone operation, the speed, which is the magnitude of the velocity, can be set, but the moving direction cannot be set, so accurate information about the drone's movement should be estimated. In this paper, we estimate the velocity of the drone moving at a constant speed and direction. In order to estimate the drone's velocity, the displacement of the target frame to minimize the sum of absolute difference (SAD) of the reference frame and the target frame is obtained. The ground truth of the drone's velocity is calculated using the position of a certain matching point over all frames. In the experiments, a video was obtained from the drone moving at a constant speed at a height of 150 meters. The root mean squared error (RMSE) of the estimated velocities in x and y directions and the RMSE of the speed were obtained showing the reliability of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.6A
• /
• pp.405-419
• /
• 2012

In this paper, we propose a joint base station(BS) and relay precoders design in multi-relay aided multi-user all-multiple-input multiple-output(MIMO) system. The design criterion is to minimize user sum mean square error(SMSE) with relay sum power constraint(RSPC) where only local channel state information(CSI)s are available at relays. Local CSI at a relay is defined as the CSI of the channel which the relay itself accesses, out of among all the 1st hop and the 2nd hop channel in the system. With BS precoder structure which is concatenated with block diagonalization(BD) precoder, each relay can determine its own precoder using only local CSI. Proposed scheme is based on sequential iteration of two stages; stage 1 determines BS precoder and relay precoders jointly with SMSE duality, and stage 2 determines user receivers. Proposed scheme can be demonstrated theoretically to be always converge. We verify that proposed scheme outperforms simple amplify-and-forward(SAF), MMSE relay, and proposed schemes in [1] in terms of both SMSE and sum-rate performances.

• Membrane and Water Treatment
• /
• v.9 no.5
• /
• pp.363-371
• /
• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.