• Tribology and Lubricants
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• v.24 no.6
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• pp.291-296
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• 2008

In order to improve the leakage prediction and rotordynamic analysis of an annular seal with a smooth rotor and circumferentially grooved stator, CFD analysis using FLUENT has been performed to determine the groove penetration angle a which is the angle of separation line between control volumes II and III in groove section of Ha and Lee's three-control-volume theory. Validation to the present analysis using new penetration angle determined by the CFD analysis is achieved by comparisons with the results of published Ha and Lee's analysis. For the leakage prediction the present analysis shows slight improvement and CFD results yields the best. Direct damping and cross-coupled stiffness coefficients are predicted better to the experimental ones. However, direct stiffness coefficient is predicted worse.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.129-134
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• 1987

In this paper, "Revised two-level costate prediction method" is developed to optimize the quadratic performance of a class of nonlinear dynamic systems. To show the merit, of this algorithm, the proposed algorithm is compared With "The new prediction method" and "Two-level costate prediction method". Advantages of this algorithm are illustrated by applying it to three examples, turbine generator system, fermentation Process, power control system in nuclear reactor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of the Korea Institute of Building Construction
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• v.18 no.3
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• pp.243-249
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• 2018

The study for curling control of plain concrete in underground parking lot was conducted in this study. The shrinkage reducing agent(SRA) was used to minimize the curling deformation of plain concrete in underground parking lot. For the quantitative curling control, the simplified prediction method applying the deflection theory of cantilever beam was proposed too, and the validity of prediction method was examined through the comparison between the experimental values and predictive values. In result, the curling of SRA 1.0% concrete was about 30% less than that of SRA 0.0% concrete, and the possibility of curling estimation by the simplified prediction method was confirmed through the comparison between the experimental values and predictive values.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.623-637
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• 2018

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.87-91
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• 2009

The welding distortion of a hull structure in the shipbuilding industry is inevitable at each assembly stage. The geometric inaccuracy caused by welding distortion tends to preclude the introduction of automation and mechanization and requires additional man-hours for adjustment work during the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method that can explicitly account for the influence of various factors on the welding distortion. The validity of this prediction method must also be clarified through experiments. For the purpose of reducing the weld-induced bending deflection, this paper proposes the plastic counter-deforming method (PCDM), which uses line heating as the optimum distortion control method. The validity of this method was substantiated by a number of numerical simulations and actual measurements.

• Journal of Welding and Joining
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• v.21 no.4
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• pp.75-79
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• 2003

The welding distortion of a hull structure in the shipbuilding industry is inevitable at each assembly stage. This geometric inaccuracy caused by the welding distortion tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding distortion. The validity of the prediction method must be also clarified through experiments. For the purpose of reducing the weld-induced bending deflection, this paper proposes the plastic counter-deforming method (PCDM) using the line heating as the optimum distortion control method. The validity of this method has been substantiated by a number of numerical simulations and actual measurements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.259-267
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• 1997

The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.