• Journal of computational fluids engineering
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• v.9 no.2
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• pp.52-61
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• 2004

In the present study, a solution algorithm for the computation of unsteady flows on unstructured meshes is presented. Dual time stepping is incorporated to achieve the second-order temporal accuracy while reducing errors associated with linearization and factorization. This allows any time step size, which is suitable for considering physical phenomena of interest. The Gauss-Seidel scheme is used to solve the linear system of equations. A special treatment based on spring analogy is made to handle meshes with high aspect-ratio cells. The present method was validated by comparing the results with experimental data and those obtained from rigid motion.

• International Journal of Contents
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• v.12 no.3
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.10
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• pp.1229-1239
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• 1999

A computational study on unsteady compressible flows has been performed by adopting a low Reynolds number $k-{\omega}$ turbulence model in conjunction with dual time stepping scheme. An explicit four-stage Runge-Kutta scheme for the Navier-Stokes equations and an approximate factorization scheme for the $k-{\omega}$ turbulence model equations are used. Computational results obtained for blade surface pressure distributions in the process of rotor-stator interaction in a turbine stage are in good agreement with extant experimental data. The effects of the wake from the stator on the boundary-layer transition over the rotor blade surface are discussed by showing that high intensity turbulence of the stator wake induces an early transition.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.59-62
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• 2002

In the present study, solution algorithms for the computation of unsteady flows on an unstructured mesh are presented. Dual time stepping is incorporated to achieve the 2-nd order temporal accuracy while reducing the linearization and the factorization errors associated with a linear solver. Hence, any time step can be used by only considering physical phenomena. Gauss-Seidel scheme is used to solve linear system of equations. Rigid motion and spring analogy method fur moving mesh are all considered and compared. Special treatments of spring analogy for high aspect ratio cells are presented. Finally, numerical results for oscillating wing are compared with experimental data.

• 한국전산유체공학회:학술대회논문집
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• 2003.08a
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• pp.18-24
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• 2003

In the present study, solution algorithms for the connotation of unsteady flows on an unstructured mesh me presented Dual time stepping is incorporated to achieve the 2-nd order temporal accuracy while reducing the linearization and the factorization errors associated with a linear solver. Hence, any time step can be used by only considering physical phenomena. Gauss-Seidel scheme is used to solve linear system of equations. Rigid motion and suing analogy method for moving mesh are all considered and compared. Special treatments of suing analogy for high aspect ratio cells are presented. Finally, numerical results for oscillating ing are compared with experimental data.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.179-186
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• 1999

Three-dimensional flow analysis is implemented to investigate the flow through transonic axial-flow compressor rotor(NASA R67), and to evaluate the performances of k-$\epsilon$ and Baldwin-Lomax turbulence models. A finite volume method is used for spatial discretization. And, the equations are solved implicitly in time with the use of approximate factorization. Upwind difference scheme is used for inviscid terms, but viscous terms are centrally differenced. The flux-difference-splitting of Roe is used to obtain fluxes at the cell faces. Numerical analysis is performed near peak efficiency and near stall. And, the results are compared with the experimental data for NASA R67 rotor. Blade-to-Blade Mach number distributions are compared to confirm the accuracy of the code. From the results, we conclude that k-$\epsilon$ model is better for the calculation of flow rate and efficiency than Baldwin-Lomax model. But, the predictions for Mach number and shock structure are almost same.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.4
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• pp.459-471
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• 1994

A numerical scheme solving the quasi-3D wave-induced circulation is presented. The governing equations have been solve implicitly using a fractional step method in conjunction with the approximate factroization techniques. The equation of each step was discretized by the finite volume scheme which yields more accurate and conservative approximations than the schemes based on finite differences. Examples of computed nearshore current patterns are presented to demonstrate the validity of the model for typical situations through comparison with laboratory experimental data (Gourlay. 1974).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.95-102
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• 2012

Interactive drama is a story which requires user's free choice and participation. In this study, we grasp user's preference by making training data that utilize characters of interactive drama. Furthermore, we describe process of implementing systems which recommend new users path of stories that correspond with their preference. We used PCA and NMF to extract characteristic of preference. The success rate of recommending was 75% with PCA, while 62.5% with NMF.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.4
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• pp.237-249
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• 2019

Confirming the direct link between neural circuit activity and animal behavior has been a principal aim of neuroscience. The genetically encoded calcium indicator (GECI), which binds to calcium ions and emits fluorescence visualizing intracellular calcium concentration, enables detection of in vivo neuronal firing activity. Various GECIs have been developed and can be chosen for diverse purposes. These GECI-based signals can be acquired by several tools including two-photon microscopy and microendoscopy for precise or wide imaging at cellular to synaptic levels. In addition, the images from GECI signals can be analyzed with open source codes including constrained non-negative matrix factorization for endoscopy data (CNMF_E) and miniscope 1-photon-based calcium imaging signal extraction pipeline (MIN1PIPE), and considering parameters of the imaged brain regions (e.g., diameter or shape of soma or the resolution of recorded images), the real-time activity of each cell can be acquired and linked with animal behaviors. As a result, GECI signal analysis can be a powerful tool for revealing the functions of neuronal circuits related to specific behaviors.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.