• Water for future
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• v.25 no.3
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• pp.65-77
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• 1992

The purpose of this study is to analyse existing hysteresis models and to propose a new type of model. The existing hysteresis models are classified by three types: interpolation model, scaling model and domain model, of which the domain model is based on the theoretical approach. Models which need one branch of hysteresis loop for calibration are developed based on the independent domain concept, however, they are not successful to accurately simulate the real data and Rubicon Sandy Loam and Dune Sand. There is a possibility that a new model is based on the dependent domain model considering the pore blockage effect against air entry for homogeneous porous media(modelIII-1, Mualem, 1984). Concludingly, a new type of hysteresis model is proposed by simplifying ModelIII-1 using a proper assumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.515-528
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• 2003

Component Based Software Development has been recognized as a new software development paradigm, and received much attention among researchers. However, it requires software architecture based development to assure component reusability and efficient software development. This paper proposes the Component Based 4+1 View Model of software architecture to support component based software development. It is redefined on the basis of the existing 4+1 view model of software architecture developed by Kruchten. Also, we describe the elements of the view model in detail with UML. This architecture constructs the foundation of component based software such as increasing the understanding of software and providing the information about how the components interact with each other. It can be done by exposing the context for the use of software components to each views.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.920-927
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• 2008

This study deals with modeling of Head-Related Transfer Functions (HRTFs) using Principal Components Analysis (PCA) in the time and frequency domains. Four PCA models based on Head-Related Impulse Responses (HRIRs), complex-valued HRTFs, augmented HRTFs, and log-magnitudes of HRTFs are investigated. The objective of this study is to compare modeling performances of the PCA models in the least-squares sense and to show the theoretical relationship between the PCA models. In terms of the number of principal components needed for modeling, the PCA model based on HRIR or augmented HRTFs showed more efficient modeling performance than the PCA model based on complex-valued HRTFs. The PCA model based on HRIRs in the time domain and that based on augmented HRTFs in the frequency domain are shown to be theoretically equivalent. Modeling performance of the PCA model based on log-magnitudes of HRTFs cannot be compared with that of other PCA models because the PCA model deals with log-scaled magnitude components only, whereas the other PCA models consider both magnitude and phase components in linear scale.

• Steel and Composite Structures
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• v.29 no.2
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• pp.175-186
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• 2018

The plastic hinge method and the plastic zone method are extensively adopted in displacement-based elements and force-based elements respectively for second-order inelastic analysis. The former enhances the computational efficiency with relatively less accurate results while the latter precisely predicts the structural behavior but generally requires more computer time. The displacement-based elements receive criticism mainly on plasticity dominated problems not only in accuracy but also in longer computer time to redistribute the forces due to formation of plastic hinges. The multi-element-per-member model relieves this problem to some extent but will induce a new problem in modeling of member initial imperfections required in design codes for direct analysis. On the contrary, a force-based element with several integration points is sufficient for material yielding. However, use of more integration points or elements associated with fiber section reduces computational efficiency. In this paper, a new force-based element equipped with stress-resultant plasticity model with minimal computational cost is proposed for second-order inelastic analysis. This element is able to take the member initial bowing into account such that one-element-per-member model is adequate and complied with the codified requirements of direct analysis. This innovative solution is new and practical for routine design. Finally, several examples demonstrate the validity and accuracy of the proposed method.

• Coupled systems mechanics
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• v.5 no.4
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• pp.355-369
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• 2016

Changes in chemical structure have profound effects on the physical properties of epoxy-based materials, and eventually affect the durability of the entire system. Microscopic structural voids generally existing in the epoxy cross-linked networks have a detrimental influence on the epoxy mechanical properties, but the relation remains elusive, which is hindered by the complex structure of epoxy-based materials. In this paper, we investigate the effect of structural voids on the epoxy-based materials by using our developed mesoscale model equipped with the concept of multiscale modeling, and SU-8 photoresist is used as a representative of epoxy-based materials. Developed from the results of full atomistic simulations, the mesoscopic model is validated against experimental measurements, which is suitable to describe the elastic deformation of epoxy-based materials over several orders of magnitude in time- and length scales. After that, a certain quantity of the structure voids is incorporated in the mesoscale model. It is found that the existence of structural voids reduces the tensile stiffness of the mesoscale epoxy network, when compared with the case without any voids in the model. In addition, it is noticed that a certain number of the structural voids have an insignificant effect on the epoxy elastic properties, and the mesoscale model containing structural voids is close to those found in real systems.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.736-743
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• 2021

Due to COVID-19, the non-face-to-face content market is growing rapidly. However, most of the non-face-to-face content such as webtoons and web novels are produced based on the traditional culture of other countries, not Korean traditional culture. The biggest cause of this situation is the lack of reference materials for creating based on Korean traditional culture. Therefore, the need for materials on traditional Korean culture that can be used for content creation is emerging. In this paper, we propose a generation model of source data based on traditional folk paintings through the fusion of traditional Korean folk paintings and AI technology. The proposed model secures basic data based on folk tales, analyzes the style and characteristics of folk tales, and converts historical backgrounds and various stories related to folk tales into data. In addition, using the built data, various new stories are created based on AI technology. The proposed model is highly utilized in that it provides a foundation for new creation based on Korean traditional folk painting and AI technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.1-8
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• 2019

Most of rotor resistance estimators utilizes Classical qd Model (CQDM) and Alternate qd Model (AQDM). The rotor resistance estimators based on both models were shown to provide an accurate rotor resistance estimate under conditions where flux is constant such as a field-oriented control (FOC) based induction motor drives. Under the conditions where flux is varying such as a Maximum torque per amp (MTPA) control, AQDM based rotor resistance estimator estimates actual rotor resistance accurately even in different operating points. However, CQDM based rotor resistance estimator has not been investigated and its performance is questionable under condition where flux level is varying. Thus, in this work, the performance of CQDM based rotor resistance estimator was investigated and made comparisons with AQDM based estimator under conditions where flux level is significantly varying such as in MTPA control based induction motor drives. Unlike AQDM based estimator, the laboratory results show that the CQDM based estimator underestimates actual rotor resistance and exhibits an undesirable dip in the estimates in different operating points.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.