• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1156-1170
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• 2019

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

• Journal of computational fluids engineering
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• v.8 no.2
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• pp.8-15
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• 2003

Three nonlinear κ-ε models with the wall function method are applied to the fully developed turbulent flow in a triangular subchannel of a bare rod bundle. Typical predicted quantities such as axial and secondary velocities, turbulent kinetic energy and wall shear stress are compared in details both qualitatively and quantitatively with both each other and experimental data. The nonlinear κ-ε models by Speziale[1] and Myong and Kasagi[2] are found to be capable of predicting accurately noncircular duct flows involving turbulence-driven secondary motion. The nonlinear κ-ε model by Shih et aL.[3] adopted in a commercial code is found to be unable to predict accurately noncircular flows with the prediction level of secondary flows one order less than that of the experiment.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.233-242
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• 2014

A magnetically levitated vehicle (Maglev) system is under commercialization as a new transportation system in Korea. The Maglev is operated by an unmanned automatic control system. Therefore, the plan of train operation should be carefully established and validated in advance. In general, when making a train operation plan, statistically predicted traffic data is used. However, a traffic wave often occurs in real train service, and demand-driven simulation technology is required to review a train operation plan and service quality considering traffic waves. We propose a method and model to simulate Maglev operation considering continuous demand changes. For this purpose, we employed a discrete event model that is suitable for modeling the behavior of railway passenger transportation. We modeled the system hierarchically using discrete event system specification (DEVS) formalism. In addition, through implementation and an experiment using the DEVSim++ simulation environment, we tested the feasibility of the proposed model. Our experimental results also verified that our demand-driven simulation technology can be used for a priori review of train operation plans and strategies.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.57.2-57.2
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• 2014

Protostellar jets and outflows are signatures of star formation and promising mechanisms for driving supersonic turbulence in molecular clouds. We quantify outflow-driven turbulence through three-dimensional numerical simulations using an isothermal version of the total variation diminishing code. We drive turbulence in real space using a simplified spherical outflow model, analyze the data through density probability distribution functions (PDFs), and investigate density and velocity power spectra. The real-space turbulence-driving method produces a negatively skewed density PDF possessing an enhanced tail on the low-density side. It deviates from the log-normal distributions typically obtained from Fourier-space turbulence driving at low densities, but can provide a good fit at high densities, particularly in terms of mass-weighted rather than volume-weighted density PDF. We find shallow density power-spectra of -1.2. It is attributed to spherical shocks of outflows themselves or shocks formed by the interaction of outflows. The total velocity power-spectrum is found to be -2.0, representative of the shock dominated Burger's turbulence model. Our density weighted velocity power spectrum is measured as -1.6, slightly less that the Kolmogorov scaling values found in previous works.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.63-69
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• 2019

Recently, ultra-low latency flash storage devices such as Z-SSD and Optane SSD were introduced with the significant technological improvement in the storage devices which provide much faster response time than today's other NVMe SSDs. With such ultra-low latency, $10{\mu}s$, storage devices the cost of context switch could be an overhead during interrupt-driven I/O completion process. As an interrupt-driven I/O completion process could bring an interrupt handling overhead, polling or hybrid-polling for the I/O completion is known to perform better. In this paper, we analyze tail latency problem in a polling process caused by process scheduling in data center environment where multiple applications run simultaneously under one system and we introduce our adaptive polling selection technique which dynamically selects efficient processing method between two techniques according to the system's conditions.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.225-235
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• 1986

Dataflow computers exhibit a high degree of parallelism which can not be obtained easily with the conventional von-Neumann architecture. Since many instructions are ready for execution simultaneously, concurrency can easily by achieved by the multiple processors modified the data-flow machine. In paper, we describe an improved dataflow architecture which is designed by adding the demand propagation network to the MIT dataflow machine. and show the improved performance by the execution time and the efficiency of processing elements through simulation with the time acceleration method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.35-42
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• 2021

This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.1-11
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• 1996

As an estimate of a location parameter for a given data set, $\alpha$-trimmed mean has been studied for a long time by many statisticians because of its nice propoerties including robustness. However, its performance depends on the proportion of trimming say $\alpha$. In this paper, we suggest a data-driven choice of $\alpha$ and study its validity. Also, we suggest a new estimator and consider double-bootstrap to improve its performance. By using simulation study, the proposed method is compared with the exiting one in various cases. Real data sets are also analyzed by using the proposed method.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.481-492
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• 2017

This paper investigates a convergence rate of a test statistics given by two scale sampling method based on $A\ddot{i}t$-Sahalia and Jacod (Annals of Statistics, 37, 184-222, 2009). This statistics tests for longitudinal data having the existence of long memory dependence driven by fractional Brownian motion with Hurst parameter $H{\in}(1/2,\;1)$. We obtain an upper bound in the Kolmogorov distance for normal approximation of this test statistic. As a main tool for our works, the recent results in Nourdin and Peccati (Probability Theory and Related Fields, 145, 75-118, 2009; Annals of Probability, 37, 2231-2261, 2009) will be used. These results are obtained by employing techniques based on the combination between Malliavin calculus and Stein's method for normal approximation.