• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• Journal of ILASS-Korea
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• v.5 no.2
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• pp.35-42
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• 2000

A number of atomization and droplet breakup models have been developed and used to predict the diesel spray characteristics. Of the many atomization and droplet breakup models based on the breakup mechanism due to aerodynamic liquid and gas interaction, four models classified as mathematical models, such as TAB, modified TAB, DDB, WB and one of the hybrid model based on WB and TAB models were selected for the assessment of prediction ability of diesel spray dynamics. The assessment of these models by using KIVA-II code was performed by comparing with the experimental data of spray tip penetration and sauter mean diameter(SMD) from the literature. It is found that the prediction of spray tip penetration and SMD by the hybrid model was only influenced by the initial parcel number. All the atomization and droplet breakup models considered here was strongly dependent on the grid resolution. Therefore it is important to check the grid resolution to get an acceptable results in selecting the models. At low injection pressure, modified TAB model could only give the good agreement with experimental data of spray tip penetration and both of modified TAB and DDB models were recommendable for the prediction of SMD. At high injection pressure, hybrid model could only give the good agreement with the experimental data of spray tip penetration and the prediction of all of the selected models did not match the experimental data. Spray tip penetration was increased with the increase the $B_1$ and the increase of $B_1$ did not affected the prediction of SMD.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.163-173
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• 2018

This paper performed the prediction of the acoustic loads applied to the surface of the flight vehicle during flight. Acoustic loads during flight arise from the pressure fluctuations on the surface of body. The conventional method of predicting the acoustic loads in flight uses semi-empirical method derived from theoretical and experimental results. However, there is a limit in obtaining the flow characteristics and the boundary layer parameters of the flight vehicle which are used as the input values of the empirical equation through experiments. Therefore, in this paper, we use the hybrid method which combines the results of CFD (Computational Fluid Dynamics) with semi-empirical methods to predict the acoustic loads acting on flight vehicle during flight. For the flight vehicle with cone-cylinder-flare shape, acoustic loads were estimated for the subsonic, transonic, supersonic, and Max-q (Maximum dynamic pressure) condition flight. For the hybrid method, two kind of boundary layer edge estimation methods based on CFD results are compared and the acoustic loads prediction results were compared according to empirical equations presented by various researchers.

• Korean Journal of Materials Research
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• v.21 no.2
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• pp.100-105
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• 2011

The relative viscosity was measured at different filler loadings for a cycloaliphatic epoxy resin and hexahydro-4-methylphthalic anhydride hardener system filled with micro/nano hybrid silica. Various empirical models were fitted to the experimental data and a fitting parameter such as critical filler fractions (${\phi}_{max}$) was estimated. Among the models, the Zhang-Evans model gave the best fit to the viscosity data. For all the silica loadings used, ln (relative viscosity) varied linearly with filler loadings. Using the Zhang-Evans model and the linearity characteristics of the viscosity change, simple methods to predict the relative viscosity below ${\phi}_{max}$ are presented in this work. The predicted viscosity values from the two methods at hybrid silica fractions of $\phi$ = 0.086 and 0.1506 were confirmed for a micro:nano = 1:1 hybrid filler. As a result, the difference between measured and predicted values was less than 11%, indicating that the proposed predicting methods are in good agreement with the experiment.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.1
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• pp.9-15
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• 2012

An unsteady internal ballistic performance model was proposed to take account for the variance of local regression rate along the grain port of a hybrid rocket combustor. The characteristic parameters of hybrid rocket motor was investigated. The performance model of concern in the study was fairly comparable with the test result. The combustion coefficients and local burning characteristics of a hybrid rocket motor were evaluated. The local variation of the oxidizer mass flow rate results in the changes of local regression rate, pressure, temperature, and gas velocity to flow direction, which was analyzed quantitatively.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.117-124
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• 2008

In early design stage, the simulation of interior noise is useful for the enhancement of the noise, vibration and harshness (NVH) performance in a vehicle. The traditional transfer path analysis (TPA) technology cannot simulate the interior noise since it uses the experimental method. In order to solve this problem, in this paper, the hybrid TPA is developed as the novel approach. The hybrid TPA uses the simulated excitation force as the input force, which excites the flexible body of a car at the mount point, while the traditional TPA uses the measured force. This simulated force is obtained by numerical analysis for the FE (finite element) model of a powertrain. The interior noise is predicted by multiplying the simulated force by the vibro-acoustic transfer function (VATF) of the vehicle. The VATF is the acoustic response in the compartment of a car to the input force at the mount point of the powertrain in the flexible car body. The trend of the predicted interior noise based on the hybrid TPA very well corresponds to the measured interior noise, although there is some difference due to not only the experimental error and the simulation error but also the effect of the air-borne path.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• Earthquakes and Structures
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• v.7 no.6
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• pp.1061-1070
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• 2014

An iterative hybrid structural dynamic reliability prediction model has been developed under multiple-time interval loads with and without consideration of stochastic structural strength degradation. Firstly, multiple-time interval loads have been substituted by the equivalent interval load. The equivalent interval load and structural strength are assumed as random variables. For structural reliability problem with random and interval variables, the interval variables can be converted to uniformly distributed random variables. Secondly, structural reliability with interval and stochastic variables is computed iteratively using the first order second moment method according to the stress-strength interference theory. Finally, the proposed method is verified by three examples which show that the method is practicable, rational and gives accurate prediction.

• Structural Engineering and Mechanics
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• v.13 no.5
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• pp.491-504
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• 2002

Recently we formulated a 2D hybrid stress element from the 3D Hellinger-Reissner principle for the analysis of thick bodies that are symmetric to the thickness direction. Polynomials have typically been used for all the displacement and stress fields. Although the element predicted the dominant stress and all displacement fields accurately, its prediction of the out-of-plane shear stresses was affected by the very high order terms used in the polynomials. This paper describes an improved formulation of the 2D element using Fourier series expansion for the out-of-plane displacement and stress fields. Numerical results illustrate that its predictions have markedly improved.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.