• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.341-346
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• 2014

This paper has presented the design and fabrication of phase correlator for wideband digital frequency discriminator (DFD) operating over the 6.0 to 18.0 GHz frequency range. Fabricated DFD phase correlator has been measured I or Q output signal, and analyzed frequency discrimination error. The operation of the proposed mixer type correlator has been analyzed by deriving some analytic equations. To design the phase correlator, this paper has modeled and simulated IQ mixer and 8-way power divider by using RF simulation tool. Designed phase correlator has fabricated and measured. The phase error and frequency discrimination error have been presented using by measured I and Q output signal. Over the 6.0~18.0 GHz range, the root mean square(RMS) phase error is $4.81^{\circ}$, RMS and frequency discrimination error is 1.49 MHz, RMS.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.329-334
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• 2007

This paper is for investigation of the noise reduction effect all 20 filters by combining Low Pass Filter(LPF) 5 kinds, High Pass Filter(HPF) 4 kinds, Median Filter(MF) 11 kinds. Noise is added to reduce noise and minimize the quality degradation in medical imaging system. We also compared Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE) for all the combinations of different filters. As a result, noise decrease appeared in the LPF3*3#1 + HPF3*3#1 + Vertical3*1 of highest filter. We confirmed noise reduction effect by applying the proposed filter combination in many fields of medical imaging applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1185-1190
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• 2008

In this paper, we propose an simplified successive minimum mean square error ($S^{2}MMSE$) algorithm that can simplify the computational complexity for precoding matrix generation in the successive minimum mean square error (SMMSE) precoding method, which is adopted as a multiuser multiple-input multiple-output (MU-MIMO) precoding technique in the IST (information society technologies)-WINNER (wireless world initiative new radio) project. The original algorithm generates the precoding matrix by calculating all individual precoding vectors with each requiring its own MMSE nulling matrix, over all receive antennas for all users. In contrast, this proposed algorithm first calculates the MMSE nulling matrix for each user, and then calculates all precoding vectors for respective receive antennas of the corresponding user by using the identical MMSE nulling matrix, in which only a simple matrix-vector multiplication is required for each vector. Consequently, it can simplify significantly the computational complexity to generate a precoding matrix for SMMSE precoding.

• Journal of Navigation and Port Research
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• v.36 no.8
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• pp.637-641
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• 2012

In this paper, the method that reduce a computation time by optimizing computation process is proposed to realize low-power underwater acoustic communication system. At first, dependency of decision delay on tap length of linear equalizer was investigated. Variance is calculated based on this result, and the optimal decision delay bound is estimated. In addition to decide optimal tap length with decision delay, we extracted the MSE(Mean Square Error) graph. From the graph, we obtained variance value of the MSE-decision delay, and estimated the optimum decision delay range from the variance value. Also, using the extracted optimal parameters, we performed a simulation. According to the result, the simulation employing optimal tap length, which is only 40% of maximum tap length, showed a satisfactory performance comparable to simulation employing maximum tap length. We verified that the proposed method has 33% lower tap length than maximal tap length via sea trial.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.109-120
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• 2004

Since the late of 1990, there have been number of studies on the required number of probe vehicles and/or optimal aggregation interval sizes for travel time estimation and forecasting. However, in general one to five minutes are used as aggregation intervals for the travel time estimation intervals for the travel time estimation and/or forecasting of loop detector system without a reasonable validation. The objective of this study is to deveop models for identifying optimal aggregation interval sizes of loop detector data for travel time estimation and prediction. This study developed Cross Valiated Mean Square Error (CVMSE) model for the link and route travel time forecasting, The developed models were applied to the loop detector data of Kyeongbu expressway. It was found that the optimal aggregation sizes for the travel time estimation and forecasting are three to five minutes and ten to twenty minutes, respectively.

• Journal of IKEEE
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• v.15 no.3
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• pp.235-240
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• 2011

The scattered pilots employed in DVB-T take advantage of the merits of both the block type and comb type pilot arrangement to increase the transmission efficiency. To estimate the channel transfer functions for data subcarriers, it is required to conduct time-frequency domain 2D estimation using the pilots. Though 2D Wiener estimator is optimal in sense of MSE (mean square error), it is too complex to implement in hardware. In this paper a new channel estimation method for the scattered pilot based OFDM system by measuring the power of AWGN and removing the noise in the LS (least square) estimate of the channel is proposed. And the simulation results reveal the proposed method outperforms the 2D linear interpolation in the fading channel.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.129-143
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• 2011

In this study, to select the incoming solar radiation equation which is most suitable for the estimation of Penman evaporation, 12 incoming solar radiation equations were selected. The Penman evaporation rates were estimated using 12 selected incoming solar radiation equations, and the estimated Penman evaporation rates were compared with measured pan evaporation rates. The monthly average daily meteorological data measured from 17 meteorological stations (춘천, 강능, 서울, 인천, 수원, 서산, 청주, 대전, 추풍령, 포항, 대구, 전주, 광주, 부산, 목포, 제주, 진주) were used for this study. To evaluate the reliability of estimated evaporation rates, mean absolute bias error(MABE), root mean square error(RMSE), mean percentage error(MPE) and Nash-Sutcliffe equation were applied. The study results indicate that to estimate pan evaporation using Penman evaporation equation, incoming solar radiation equation using meteorological data such as precipitation, minimum air temperature, sunshine duration, possible duration of sunshine, and extraterrestrial radiation are most suitable for 11 study stations out of 17 study stations.