• Journal of Positioning, Navigation, and Timing
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• 제10권3호
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• pp.197-206
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• 2021

We compared two typical ensemble time scale algorithms; AT1 and Kalman filter. Four commercial atomic clocks composed of two hydrogen masers and two cesium atomic clocks provided measurement data to the algorithms. The allocation of relative weights to the clocks is important to generate a stable ensemble time. A 30 day-average-weight model, which was obtained from the average Allan variance of each clock, was applied to the AT1 algorithm. For the reduced Kalman filter (Kred) algorithm, we gave the same weights to the two hydrogen masers. We also compared the frequency stabilities of the outcome from the algorithms when the frequency offsets and/or the frequency drift offsets estimated by the algorithms were corrected or not corrected by the KRISS-made primary frequency standard, KRISS-F1. We found that the Kred algorithm is more effective to generate a stable ensemble time scale in the long-term, and the algorithm also generates much enhanced short-term stability when the frequency offset is used for the calculation of the Allan deviation instead of the phase offset.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 대한의용생체공학회:의공학회지
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• 제5권1호
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• pp.5-8
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• 1984

As the ensemble averaged dz/dt signal during exercise gets smoothed, it is difficult to find the distinctive marks for estimation of stroke volume. The cross correlation function was made use of estmating these marks for automatic calculation by computer from the ensemble averaged dz/dt signal. LVET(Left Ventricular Ejection Time) and stroke volume were estimated based on the calculated parameters from the characteristic points. LVET, stroke volume calculated by hand, by the ensemble average and the cross correlation were compared for accuracy validation.

• 대한의용생체공학회:의공학회지
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• 제12권4호
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• pp.261-266
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• 1991

As the ensemble averaged dz/dt signal during exercise gets smoothed, it is difficult to find the distinctive marks for estimation of stroke volume. The cross correlation function was made use of estimating these marks for automatic calculation by computer from the ensemble averaged dz/dt signal. LVET( Left Ventricular Ejection Time) and stroke volume were estimated based on the calculated parameters from the characteristic points. LVET, stroke volume calculated by hand, by the ensemble average and the cross correlation were compared for accuracy validation.

• 대기
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• 제28권4호
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• Journal of electromagnetic engineering and science
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• 제13권3호
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• pp.192-194
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• 2013

This paper proposes a validation model using a reverberation chamber for verification of the effectiveness and accuracy of the statistical power balance (PWB) method. The PWB method treats electromagnetic (EM) field distributions in certain environments as a representative value of an "ensemble" average, so a reverberation chamber is utilized as a testing apparatus to experimentally simulate the sets of measurement field "ensemble" inside a complex environment. Measurements are performed with various test conditions for environmental loss using flat absorbers, and front and side doors of the chamber. Comparison between the PWB analysis and the measurement results shows good agreement, indicating the usefulness of this statistical analysis method for stating the electromagnetic field inside the EM zone as a representative value to general structures.

• 한국의류학회지
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• 제26권6호
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• pp.763-770
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• 2002

The objective of the study was to investigate skin temperature responses of Hanbok when it was worn. Two healthy females(average 21 years, 155cm and 60kg were exposed to a climatic chamber(Room Temp. $21{\pm}1^{\circ}C,\;52{\pm}2%R.H.$, 0.15m/s). During the experiment, rectal temperature, skin temperature of 9 areas, clothing microclimate, subjective sensation were measured. Chima and Jogory to be made of silk nobang(SN) or Ramie were worn for summer. Polyester(P) Chima and Jogori(R) could be wort for spring and autumn. For winter, silk Chima, Jogori(S) and Durumagi(D) were commonly worn. Rectal temperature was high in order of naked(N), R, SN, P, S, D. However Mean skin temperature was reversely high in order of D, S, SN, R, P, naked. In naked, skin temperature was high in order of head, trunk upper extremity and lower extremity. But on wearing of Hanbok, it was the highest at the chest except head regardless of kinds of clothing ensembles. Skin temperature of upper arm was secondly highest on wearing the silk ensemble and the Durumagi ensemble, but skin temperature of buttock was secondly highest on wearing the silk nobang ensemble and the ramie ensemble. Skin temperature on wearing the silk ensemble was generally higher than those on other clothing ensembles. Local and mean skin temperatures on wearing the silk ensemble and the Durumagj ensemble were generally higher than on other clothing ensembles. Heat resistance of the fabric might have affected on the local skin temperature.