• Structural Engineering and Mechanics
• /
• 제36권4호
• /
• pp.481-497
• /
• 2010

The shaking table tests were conducted on two small-scale models (Model 1 and Model 2) to examine the earthquake-induced damage of a concrete gravity dam, which has been planned for the construction with the recommendation of the peak ground acceleration of the maximum credible earthquake of 0.42 g. This study deals with the numerical simulation of shaking table tests for two smallscale dam models. The plastic damage constitutive model is used to simulate the crack/damage behavior of the bentonite-concrete mixture material. The numerical results of the maximum failure acceleration and the crack/damage propagation are compared with experimental results. Numerical results of Model 1 showed similar crack/damage propagation pattern with experimental results, while for Model 2 the similar pattern was obtained by considering the modulus of elasticity of the first and second natural frequencies. The crack/damage initiated at the changing point in the downstream side and then propagated toward the upstream side. Crack/damage accumulation occurred in the neck area at acceleration amplitudes of around 0.55 g~0.60 g and 0.65 g~0.675 g for Model 1 and Model 2, respectively.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.177-182
• /
• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Nuclear Engineering and Technology
• /
• 제36권3호
• /
• pp.248-262
• /
• 2004

Turbulence models are separately assessed for a three dimensional thermal-hydraulic analysis of the integral reactor SMART. Seven models (mixing length, k-l, standard $k-{\epsilon},\;k-{\epsilon}-f{\mu},\;k-{\epsilon}-v2$, RRSM, and ERRSM) are investigated for flat plate channel flow, rotating channel flow, and square sectioned U-bend duct flow. The results of these models are compared to the DNS data and experiment data. The results are assessed in terms of many aspects such as economical efficiency, accuracy, theorization, and applicability. The standard $k-{\epsilon}$ model (high Reynolds model), the $k-{\epsilon}-v2$ model, and the ERRSM (low Reynolds models) are selected from the assessment results. The standard $k-{\epsilon}$ model using small grid numbers predicts the channel flow with higher accuracy in comparison with the other eddy viscosity models in the logarithmic layer. The elliptic-relaxation type models, $k-{\epsilon}-v2$, and ERRSM have the advantage of application to complex geometries and show good prediction for near wall flows.

• International Journal of Aerospace System Engineering
• /
• 제2권2호
• /
• pp.67-72
• /
• 2015

The main concern of this study is to integrate the EARSM into an industrial RANS solver in conjunction with the $k-{\omega}$ model, as proposed by Hellsten (EARSMKO2005). In order to improve the robustness, particular limiters are introduced to turbulent conservative variables, and a suitable full-approximation storage (FAS) multi-grid (MG) strategy is designed to incorporate turbulence model equations. The present limiters and MG strategy improve both robustness and efficiency significantly but without degenerating accuracy. Two discretization approachs for velocity gradient on cell interfaces are implemented and compared with each other. Numerical results of a three-dimensional supersonic square duct flow show that the proper discretization of velocity gradient improves the accuracy essentially. To assess the capability of the resulting EARSM $k-{\omega}$ model to predict complex engineering flow, the case of Common Research Model (CRM, Wing-Body) is performed. All the numerical results demonstrate that the resulting model performs well and is comparable to the standard two-equation models such as SST $k-{\omega}$ model in terms of computational effort, thus it is suitable for industrial applications.

• Food Science and Biotechnology
• /
• 제15권5호
• /
• pp.715-720
• /
• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• 한국물환경학회지
• /
• 제24권6호
• /
• pp.784-792
• /
• 2008

Greater focus must be placed on ensuring that the water quality model (WQM) reflects the objective of its application and the characteristics of the water environment properly before it is selected. In the development or application of WQM, various factors influencing the model predictions should be reviewed so that it can perform more properly and reasonably based on scientific theory. This study reviewed the characteristic of existing WQM and the domestic river environment to find the requirements of the model application for TMDLs management in Korea. In this study, a water quality model, QUAL-NIER, was developed based on the USEPA's QUAL2E. The core structure and reaction scheme of the model was established followed by the formulation of equations according to the scheme with some supplements on the reaction mechanisms which are necessary for domestic rivers. Algorithms on the equations were set up and programmed to form a computer-based model. The developed model, QUAL-NIER was applied to the main stem of the Nakdong river. The model was calibrated and verified to data measured in 2004. The model results displayed good agrement with the field measurements for both calibration and verification. From this study, it was concluded that the developed QUAL-NIER model was very powerful with regard to the water quality simulation in domestic rivers.

• Maxillofacial Plastic and Reconstructive Surgery
• /
• 제29권6호
• /
• pp.520-526
• /
• 2007

The accuracy of model surgery is one of important factors which can influence the outcome of orthognathic surgery. To evaluate the accuracy of digitalized model surgery, we tried the model surgery on a software after transferring the mounted model block into a digital model, and compared the results with that of classical manual model surgery. We could get the following results, which can be used as good baseline analysis for the clinical application. 1. We made the 3D scanning of dental model blocks, and mounted on a software. And we performed the model surgery according to the previously arranged surgical plans, and let the rapid prototyping machine produce the surgical wafer. All through these process, we could confirm that the digital model surgery is feasible without difficulties. 2. The digital model surgery group (Group 2) showed a mean error of $0.0{\sim}0.1mm$ for moving the maxillary model block to the target position. And Group 1, which was done by manual model surgery, presented a mean error of $0.1{\sim}1.2mm$, which is definitely greater than those of Group 2. 3. Remounted maxillary model block with the wafers produced by digital model surgery from Group 2 showed the less mean error (0.2 to 0.4 mm) than that produced by manual model surgery in Group 1 (0.3 to 1.4 mm). From these results, we could confirm that the digital model surgery in Group 2 presented less error than manual model surgery of Group 1. And the model surgery by digital manipulation is expected to have less influence from the individual variation or degree of expertness. So the increased accuracy and enhanced manipulability will serve the digital model surgery as the good candidate for the improvement and replacement of the classical model surgery, if careful preparation works for the clinical adjustment is accompanied.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권1호
• /
• pp.366-381
• /
• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Structural Engineering and Mechanics
• /
• 제65권2호
• /
• pp.173-181
• /
• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• ETRI Journal
• /
• 제36권3호
• /
• pp.479-489
• /
• 2014

This paper presents a model-based image steganography method based on Watson's visual model. Model-based steganography assumes a model for cover image statistics. This approach, however, has some weaknesses, including perceptual detectability. We propose to use Watson's visual model to improve perceptual undetectability of model-based steganography. The proposed method prevents visually perceptible changes during embedding. First, the maximum acceptable change in each discrete cosine transform coefficient is extracted based on Watson's visual model. Then, a model is fitted to a low-precision histogram of such coefficients and the message bits are encoded to this model. Finally, the encoded message bits are embedded in those coefficients whose maximum possible changes are visually imperceptible. Experimental results show that changes resulting from the proposed method are perceptually undetectable, whereas model-based steganography retains perceptually detectable changes. This perceptual undetectability is achieved while the perceptual quality - based on the structural similarity measure - and the security - based on two steganalysis methods - do not show any significant changes.