• Smart Structures and Systems
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• v.13 no.1
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• pp.137-154
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• 2014

Servohydraulic shaking tables are being increasingly used in the field of earthquake engineering. They play a critical role in the advancement of the research state and remain one of the valuable tools for seismic testing. Recently, the National Earthquake Engineering Research Center, CGS, has acquired a 6.1m x 6.1 m shaking table system which has a six degree-of-freedom testing capability. The maximum specimen mass that can be tested on the shaking table is 60 t. This facility is designed specially for testing a complete civil engineering structures, substructures and structural elements up to collapse or ultimate limit states. It can also be used for qualification testing of industrial equipments. The current paper presents the main findings of the experimental shake-down characterization testing of the CGS shaking table. The test program carried out in this study included random white noise and harmonic tests. These tests were performed along each of the six degrees of freedom, three translations and three rotations. This investigation provides fundamental parameters that are required and essential while elaborating a realistic model of the CGS shaking table. Also presented in this paper, is the numerical model of the shaking table that was established and validated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.43-53
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• 2007

Internet becomes more and more popular, and most companies and institutes use web services for e-business and many other purposes. With the explosion of Internet, the occurrence of cyber terrorism has grown very rapidly. Simulation is one of the most widely used method to study internet worms. But, it is quite challenging to simulate very large-scale worm attacks because of various reasons. In this paper, we propose a hybrid modeling method for RCS(Random Constant Spreading) worm simulation. The proposed hybrid model simulates worm attacks by synchronizing modeling network and packet network. So, this model will be both detailed enough to generate realistic packet traffic, and efficient enough to model a worm spreading through the Internet. Moreover, our model have the capability of dynamic updates of the modeling parameters. Finally, we simulate the hybrid model with the CodeRed worm to show validity of our proposed model for RCS worm simulation.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1137-1146
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• 2010

There are a lot of methods to predict the result of a game and many forecasting researches have been studied. Among many methods, if a statistical model including some realistic random variables is used to forecast, more accurate prediction could be expected than any others. In this work, Bradley-Terry model is considered to predict results of 2010 South Africa World Cup games via paired comparison method. This prediction model includes some random variables which affect the results of games. The worth parameters for each country in this model are convergence values obtained by using Newton-Raphson algorithm. With this model, we can forecast top 16 among 32 countries and up to who will win the victory. Final results of 2010 South Africa World Cup games are compared with this prediction and discuss further works.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.762-772
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• 2011

In this paper, a method of rate control for constant bitrate (CBR) coding of High Efficiency Video Coding (HEVC) is addressed. The existing rate control of H.264/AVC may not provide exact rate control in the case of hierarchical coding structure since it doesn't consider the characteristics of the hierarchical coding structure. It is expected that a rate control is added to the reference software called HM for CBR encoding in the near future. More accurate rate control may be required in a hierarchical structure of random access (RA) mode defined in the common test condition of HM. In this paper, we propose a method of rate control based on quadratic Rate-Distortion (R-D) model considering temporal layers and frame types in hierarchical coding structure for efficient rate control. In the consideration of the trade-off relationship between the bit fluctuation and the average PSNR, both of frame and coding unit (CU) are set as the basic unit of rate control. The performance of the proposed rate control method is verified by simulations along with the trade-off relationships for the both cases of basic unit.

• Computers and Concrete
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• v.24 no.5
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• pp.413-422
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• 2019

In this study, 3D Meso-scale finite-element model is presented to study the mechanical behavior of steel microfiber-reinforced polymer concrete considering the random distribution of fibers in the matrix. The composite comprises two separate parts which are the polymer composite and steel microfibers. The polymer composite is assumed to be homogeneous, which its mechanical properties are measured by performing experimental tests. The steel microfiber-polymer bonding is simulated with the Cohesive Zone Model (CZM) to offer more-realistic assumptions. The CZM parameters are obtained by calibrating the numerical model using the results of the experimental pullout tests on an individual microfiber. The accuracy of the results is validated by comparing the obtained results with the corresponding values attained from testing the steel microfiber-reinforced polymer concrete incorporating 0, 1 and 2% by volume of microfibers, which indicates the excellent accuracy of the current proposed model. The results show that the microfiber aspect ratio has a considerable effect on the mechanical properties of the reinforced polymer concrete. Applying microfibers with a higher aspect ratio improves the mechanical properties of the composite considerably especially when the first crack appears in the polymer concrete specimens.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.585-597
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• 2008

The aim of this study was to find an analytic solution to the problem of determining the optimal capacity (lot-size) of a batch-storage network to meet demand for a finished product in a system undergoing random failures of operating time and/or batch material. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to short-term random variations in the cycle time and batch size as well as long-term variations in the average trend. Some of the production processes have random variations in product quantity. The spoiled materials are treated through regeneration or waste disposal processes. All other processes have random variations only in the cycle time. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis, the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.471-488
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• 2018

In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

• Steel and Composite Structures
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• v.42 no.2
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• pp.209-219
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• 2022

Offshore platforms in seismically active areas must be designed to survive in the face of intense earthquakes without a global structural collapse. This paper scrutinizes the seismic performance of a newly designed and established jacket type offshore platform situated in the entrance of the Gulf of Suez region based on the API-RP2A normalized response spectra during seismic events. A nonlinear finite element model of a typical jacket type offshore platform is constructed taking into consideration the effect of structure-soil-interaction. Soil properties at the site were manipulated to generate the pile lateral soil properties in the form of load deflection curves, based on API-RP2A recommendations. Dynamic characteristics of the offshore platform, the response function, output power spectral density and transfer functions for different elements of the platform are discussed. The joints deflection and acceleration responses demands are presented. It is generally concluded that consideration of the interaction between structure, piles and soil leads to higher deflections and less stresses in platform elements due to soil elasticity, nonlinearity, and damping and leads to a more realistic platform design. The earthquake-based analysis for offshore platform structure is essential for the safe design and operation of offshore platforms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2838-2858
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• 2019

Traditional channel models for vehicle-to-vehicle (V2V) communication usually assume fixed velocity in static scattering environment. In the realistic scenarios, however, time-variant velocity for V2V results in non-stationary statistical properties of wireless channels. Dynamic scatterers with random velocities and directions have been always utilized to depict the non-stationary statistical properties of the channel. In this paper, a non-stationary geometry-based cooperative scattering channel model is proposed for multiple-input multiple-output (MIMO) V2V communication systems, where a birth-death process is used to capture the appearance and disappearance dynamic properties of moving scatterers that reflect the time-variant time correlation and Doppler spectrum characteristics. Moreover, our model has more straight and concise to study the impact of the vehicular traffic density on channel characteristics and thus avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions. The numerical results validate our analysis and demonstrate that setting important parameters of our model can appropriately build up more purposeful measurement campaigns in the future.