• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.410-417
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• 2009

The objective of this paper is to study the discharge phenomenon for a point-plane air interval using an original fuzzy logic system. Firstly, a physical model based on streamer theory with consideration of the space charge fields due to electrons and positive ions is proposed. To test this model we have calculated the breakdown threshold voltage for a point-plane air interval. The same model is used to determine the discharge steps for different configurations as an inference data base. Secondly, using results obtained by the numerical simulation of the previous model, we have introduced the fuzzy logic technique to predict the breakdown threshold voltage of the same configurations used in the numerical model and make estimation on the insulating state of the air interval. From the comparison of obtained results, we can conclude that they are in accordance with the experimental ones obtained for breakdown discharges in different point-plane air gaps collected from the literature. The proposed study using fuzzy logic technique shows a good performance in the analysis of different discharge steps of the air interval.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.6
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• pp.816-821
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• 2015

In this paper, an in-flight prediction method of thrust profiles for solid rocket motors is proposed. Actually, it is very difficult to have detailed information about the performance of the rocket motors beforehand because it is quite sensitive to combustion environments. To overcome this problem, we have developed an algorithm for generating in-flight prediction of rocket motor performance in realistic environments via a reference burnback profile and accelerations measured at a short time-interval just after launch. The performance is evaluated through a lot of flight test results.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.203-209
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• 2009

Frailty models are useful for the analysis of correlated and/or heterogeneous survival data. However, the inferences of fixed parameters, rather than random effects, have been mainly studied. The prediction (or estimation) of random effects is also practically useful to investigate the heterogeneity of the hospital or patient effects. In this paper we propose how to extend the prediction method for random effects in HGLMs (hierarchical generalized linear models) to log-normal semiparametric frailty models with nonparametric baseline hazard. The proposed method is demonstrated by a simulation study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1376-1391
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• 2015

In bus delay-tolerant networks, the route of bus is determinate but its arrival time is indeterminate. However, most conventional approaches predict future contact without considering its uncertainty, which makes a limitation on routing performance. A novel approach is proposed by employing interval algebra to characterize the contact's uncertainty and time-varying nature. The contact is predicted by using the Bayesian estimation to achieve a better routing performance. Simulation results show that this approach achieves a good balance between delivery latency and delivery ratio.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.439-446
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• 2015

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of Environmental Science International
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• v.23 no.12
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• pp.2045-2056
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• 2014

In this study, pressure drop was measured in the pulse jet bag filter without venturi on which 16 numbers of filter bags (Ø$140{\times}850{\ell}$) are installed according to operation condition(filtration velocity, inlet dust concentration, pulse pressure, and pulse interval) using coke dust from steel mill. The obtained 180 pressure drop test data were used to predict pressure drop with multiple regression model so that pressure drop data can be used for effective operation condition and as basic data for economical design. The prediction results showed that when filtration velocity was increased by 1%, pressure drop was increased by 2.2% which indicated that filtration velocity among operation condition was attributed on the pressure drop the most. Pressure was dropped by 1.53% when pulse pressure was increased by 1% which also confirmed that pulse pressure was the major factor affecting on the pressure drop next to filtration velocity. Meanwhile, pressure drops were found increased by 0.3% and 0.37%, respectively when inlet dust concentration and pulse interval were increased by 1% implying that the effects of inlet dust concentration and pulse interval were less as compared with those changes of filtration velocity and pulse pressure. Therefore, the larger effect on the pressure drop the pulse jet bag filter was found in the order of filtration velocity($V_f$), pulse pressure($P_p$), inlet dust concentration($C_i$), pulse interval($P_i$). Also, the prediction result of filtration velocity, inlet dust concentration, pulse pressure, and pulse interval which showed the largest effect on the pressure drop indicated that stable operation can be executed with filtration velocity less than 1.5 m/min and inlet dust concentration less than $4g/m^3$. However, it was regarded that pulse pressure and pulse interval need to be adjusted when inlet dust concentration is higher than $4g/m^3$. When filtration velocity and pulse pressure were examined, operation was possible regardless of changes in pulse pressure if filtration velocity was at 1.5 m/min. If filtration velocity was increased to 2 m/min. operation would be possible only when pulse pressure was set at higher than $5.8kgf/cm^2$. Also, the prediction result of pressure drop with filtration velocity and pulse interval showed that operation with pulse interval less than 50 sec. should be carried out under filtration velocity at 1.5 m/min. While, pulse interval should be set at lower than 11 sec. if filtration velocity was set at 2 m/min. Under the conditions of filtration velocity lower than 1 m/min and high pulse pressure higher than $7kgf/cm^2$, though pressure drop would be less, in this case, economic feasibility would be low due to increased in installation and operation cost since scale of dust collection equipment becomes larger and life of filtration bag becomes shortened due to high pulse pressure.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.99-104
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• 2006

Young's modulus of composite has been predicted by Eshelby's equivalent inclusion method modified with Mori-Tanaka's mean field theory, where short fibers of aspect ratio distribution are assumed to be aligned. Young's modulus of the composite is predicted with the smallest class interval for simulating the actual distribution of fiber aspect ratio, which is compared with that computed using different class intervals. Young's modulus of the composite predicted with mean aspect ratio or the largest class interval is overestimated by the maximum 10%. As the class interval of short fibers for predicting Young's modulus decreases, the predicted results show good agreements with those obtained using the actual distribution of fiber aspect ratio. It can be finally concluded from the study that if and only if the class interval of short fiber normalized by the maximum aspect ratio is smaller than 0.1, the predicted results are consistent with those obtained using the actual distribution of aspect ratio.