• Advances in Energy Research
• /
• v.8 no.2
• /
• pp.111-123
• /
• 2022

Solar Energy is the energy of solar radiation carried by them in the form of heat and light. It can be converted into electricity. Solar potential depends on the site's atmosphere; the solar energy distribution depends on many factors, e.g., turbidity, cloud types, pollution levels, solar altitude, etc. We estimated solar radiation with the help of the Ashrae clear-sky model for three locations in Pakistan, namely Pasni, Gwadar, and Jiwani. As these locations are close to each other as compared to the distance between the sun and earth, therefore a slight change of latitude and longitude does not make any difference in the calculation of direct beam solar radiation (BSR), diffuse solar radiation (DSR), and global solar radiation (GSR). A modified formula for declination angle is also developed and presented. We also created two different models for Ashrae constants. The values of these constants are compared with the standard Ashrae Model. A good agreement is observed when we used these constants to calculate BSR, DSR, GSR, the Root mean square error (RMSE), Mean Absolute error (MABE), Mean Absolute percent error (MAPE), and chisquare (χ²) values are in acceptance range, indicating the validity of the models.

• ETRI Journal
• /
• v.36 no.5
• /
• pp.721-729
• /
• 2014

We propose a novel phase-based method for single-channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase-dependent a priori signal-to-noise ratio (SNR) is estimated in the log-mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase-dependent estimator is incorporated into the conventional magnitude-based decision-directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one-frame delay of the estimated phase-dependent a priori SNR by using a minimum mean square error (MMSE)-based and maximum a posteriori (MAP)-based estimator. In our speech enhancement experiments, the proposed phase-dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE-based and MAP-based estimator cases as compared to a conventional magnitude-based estimator.

• Journal of Environmental Science International
• /
• v.20 no.1
• /
• pp.93-106
• /
• 2011

Physically-based resampling scheme for roughness coefficient of surface runoff considering the spatial landuse distribution was suggested for the purpose of effective operational application of recent grid-based distributed rainfall runoff model. Generally grid scale(mother scale) of hydrologic modeling can be greater than the scale (child scale) of original GIS thematic digital map when the objective basin is wide or topographically simple, so the modeler uses large grid scale. The resampled roughness coefficient was estimated and compared using 3 different schemes of Predominant, Composite and Mosaic approaches and total runoff volume and peak streamflow were computed through distributed rainfall-runoff model. For quantitative assessment of biases between computational simulation and observation, runoff responses for the roughness estimated using the 3 different schemes were evaluated using MAPE(Mean Areal Percentage Error), RMSE(Root-Mean Squared Error), and COE(Coefficient of Efficiency). As a result, in the case of 500m scale Mosaic resampling for the natural and urban basin, the distribution of surface runoff roughness coefficient shows biggest difference from that of original scale but surface runoff simulation shows smallest, especially in peakflow rather than total runoff volume.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.923-932
• /
• 2013

This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.837-839
• /
• 2014

In radar systems, information of three-dimensional (3D) trajectory is necessary for tracking targets. The information of 3D trajectory for a 3D radar can be obtained by estimating the azimuth angle, the elevation angle, and the distance. The estimated information of the angles and the distance has errors according to received signals. Since these errors affect performances of 3D radar systems, performance analysis of 3D radar for the angles and the distance errors is required. In this paper, the performance of 3D radar systems is analyzed by root mean square error (RMSE) between true trajectory information and the estimated trajectory information according to the angles and the distance errors.

• Journal of Advanced Navigation Technology
• /
• v.16 no.6
• /
• pp.982-987
• /
• 2012

In this paper, the SNR estimation error of Decision Directed SNR estimation method in AWGN is investigated, which uses samples received in reference decision region. In communication system receiver, when SNR estimation scheme using error vectors between ideal sample points and received sample points of reference region is adopted, the samples contain incorrectly received samples due to AWGN. Consequently, the mean of estimated reference constellation point is shifted and Decision Directed SNR estimation is inaccurately performed. These effects are explained by modified probability density function and difference between actual SNR and estimated SNR is theoretically derived and quantatively analyzed. It is proved that SNR estimation error obtained through computer simulation is matched up with derived one, and SNR estimation performance is enhanced significantly by adopting suggested correction scheme.

• Structural Engineering and Mechanics
• /
• v.64 no.3
• /
• pp.279-285
• /
• 2017

Modal expansion technique (MET) is a method to estimate the vibration fields of flexible structures by using eigenmodes of the structure and the signals of sensors. It is the useful method to estimate the vibration fields but has the truncation error since it only uses the limit number of the eigenmodes in the frequency of interest. Even though block-wise MET performed frequency block by block with different valid eigenmodes was developed, it still has the truncation error due to the absence of other eigenmodes. Thus, this paper suggested an improved block-wise modal expansion technique. The technique recovers the truncation errors in one frequency block by utilizing other eigenmodes existed in the other frequency blocks. It was applied for estimating the vibration fields of a cylindrical shell. The estimated results were compared to the vibration fields of the forced vibration analysis by using two indices: the root mean square error and parallelism between two vectors. These indices showed that the estimated vibration fields of the improved block-wise MET more accurately than those of the established METs. Especially, this method was outstanding for frequencies near the natural frequency of the highest eigenmode of each block. In other words, the suggested technique can estimate vibration fields more accurately by recovering the truncation errors of the established METs.

• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.4
• /
• pp.274-282
• /
• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.

• The Mathematical Education
• /
• v.37 no.1
• /
• pp.1-13
• /
• 1998

In this paper, we have represented the efficient way how to enumerate the optimal number-right scores to adjust the item difficulty and to improve item discrimination. To estimate the optimal number-right scores in two equivalent math-tests by linear score equating a measurement error model was applied to the true scores observed from a pair of equivalent math-tests assumed to measure same trait. The model specification for true scores which is represented by the bivariate model is a simple regression model to inference the optimal number-right scores and we assume again that the two simple regression lines of raw scores and true scores are independent each other in their error models. We enumerated the difference between mean value of $\chi$＊ and ${\mu}$$\_$$\chi$/ and the difference between the mean value of y＊and a＋b${\mu}$$\_$$\chi$/ by making an inference the estimates from 2 error variable regression model. Furthermore, so as to distinguish from the original score points, the estimated number-right scores y’$\^$*/ as the estimated regression values of true scores with the same coordinate were moved to center points that were composed of such difference values with result of such parallel score moving procedure as above mentioned. We got the asymptotically normal distribution in Figure 5 that was represented as the optimal distribution of the optimal number-right scores so that we could decide the optimal proportion of number-right score in each item. Also by assumption that equivalence of two tests is closely connected to unidimensionality of a student’s ability. we introduce new definition of trait score to evaluate such ability in each item. In this study there are much limitations in getting the real true scores and in analyzing data of the bivariate error model. However, even with these limitations we believe that this study indicates that the estimation of optimal number right scores by using this enumeration procedure could be easily achieved.

• Journal of Korea Water Resources Association
• /
• v.37 no.7
• /
• pp.581-587
• /
• 2004

An experimental watershed was selected and sustainable yield was evaluated. The study area(3.89$\textrm{km}^2$) was located in Kyungpook Sangju Yangchon-dong. The visual MODFLOW was verified by comparing the observed and estimated groundwater table. The analysis of the observed and estimated groundwater table from 19 March 2003 to 18 March 2004 showed that the average error was 0.0009m, the error sum of squares 7.245$m^2$, absolute mean error 0.094 m, root mean square error 0.141m, and the model efficiency was 92%. The normal, 10- and 30- year drought frequency years were selected and sustainable yield was evaluated in these periods. Ratios of sustainable yield to the annual infiltration were 14.5% for the normal year(1992), 15.1% for the 10-year(1994), and 15.2% for the 30-year drought frequency year(1982). The results of this study can be used as a basic information for groundwater development and management planning considering regional characteristics.