• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.215-227
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• 2014

In modern computer architectures, caches are widely used to shorten the gap between processor speed and memory access time. However, caches are time-unpredictable, and thus can significantly increase the complexity of worst-case execution time (WCET) analysis, which is crucial for real-time systems. This paper proposes a time-predictable two-level scratchpad-based architecture and an ILP-based static memory objects assignment algorithm to support real-time computing. Moreover, to exploit the load/store latencies that are known statically in this architecture, we study a Scratch-pad Sensitive Scheduling method to further improve the performance. Our experimental results indicate that the performance and energy consumption of the two-level scratchpad-based architecture are superior to the similar cache based architecture for most of the benchmarks we studied.

• Journal of computational fluids engineering
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• v.11 no.3 s.34
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• pp.59-63
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• 2006

Accurate prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Loman(B-L), Spalart-Allmaras(S-A), k-$\varepsilon$, k-$\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control fins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.811-818
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• 2010

In this study, a self-administered checklist for evaluation of the musculoskeletal disorders risk factors in construction industry was developed, and its reliability and validity were studied. 10 items of the checklist were determined based on the literature review, and total 2,793 construction workers participated in the analysis of the checklist's applicability. The results from the reliability analysis showed high Cohen's kappa coefficient (0.50~0.77), and high validity was also obtained in terms of relative risk (RR 1.73~9.14). Positive predictability was relatively low (13.0~32.5%), while negative predictability was high (80.1~96.8%). It can be concluded that the checklist would be suitable as a quick filtering tool of the ergonomic risk factors.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.579-584
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• 2000

Algebraic Reynolds Stress (ARS) model is applied in order to analyze the turbulent flow of wall-attaching offset jet and to evaluate the model's predictability. The applied numerical schemes are upwind scheme and skew-upwind scheme. The numerical results show good prediction in first order calculations (i.e., reattachment length, mean velocity, pressure), while they show slight deviations in second order (i.e., kinetic energy and turbulence intensity). By comparison with the previous results using $k-{\varepsilon}$ model, ARS model predicts better than the standard $k-{\varepsilon}$ model, however, predicts slightly worse than the $k-{\varepsilon}$ model including the streamline curvature modification. Additionally this study can reconfirm that skew-upwind scheme has approximately 25% improved predictability than upwind scheme.

• Korean Journal of Head & Neck Oncology
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• v.8 no.2
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• pp.97-105
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• 1992

One hundred and thirty-nine thyroid nodules were evaluated by aspiration biopsy cytology (ABC) and were compared with the postoperative histologic diagnosis during the period from May 1, 1986 through Aug. 31, 1992. The correlation betwen the two diagnoses proved to be comparable with a low incidence of false-negative diagnoses, but with a relatively high incidence of false-positive ones. The sensitivity was 93.5%, specificity 89.6%, false-negative rate 6.5%, false-positive rate 10.4%, positive predictability 87.9%, negative predictability 94.5%, and overall diagnostic accuracy 91.4%.

• KDI Journal of Economic Policy
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• v.37 no.4
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• pp.1-20
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• 2015

We examine the validity of popular exchange rate models such as the purchasing power parity (PPP) hypothesis and the monetary model for Korean won/US dollar exchange rate. Various specification tests demonstrate that Korean data are more favorable for both models based on time-varying cointegration coefficients as compared to those based on constant cointegration coefficients. When the abilities to predict future exchange rates between those models based on time-varying cointegration coefficients are compared, an in-sample analysis shows that the time-varying PPP (monetary model) has better predictive power over horizons shorter (longer) than one year. Results from an out-of-sample analysis indicate that the time-varying PPP outperforms models based on constant cointegration coefficients when predicting future exchange rate changes in the long run.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.12
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• pp.1615-1624
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• 2000

Algebraic Reynolds Stree (ARS) model is applied in order to analyze the turbulent flow of wall-attaching offset jet and to evaluate the predictability of model. The applied numerical schemes are the upwind scheme and the skew-upwind scheme. The numerical results show a good prediction in the first order calculations(i.e., reattachment length, mean velocity, pressure), however, slight deviations in the second order(i.e., kinetic energy and turbulence intensity). Comparing with the previous results using the k-$\varepsilon$ model, the ARS model predicts better than the standard k-$\varepsilon$ model, however, slightly worse than the k-$\varepsilon$ model including the streamline curvature modification. Additionallay this study can reconfirm that the skew-upwind scheme has approximately 25% improved predictability than the upwind scheme.

• Atmosphere
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• v.16 no.2
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• pp.141-157
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• 2006

A revolution in weather and climate forecasting is in progress. This has been made possible as a result of theoretical advances in our understanding of the predictability of weather and climate, and by the extraordinary developments in supercomputer technology. New problem areas have been discovered and different solutions have been found by the recent high performance computers whose performance has been increased rapidly. Such advances in the computational performance may change the strategy of development of numerical models and prediction methods. This paper discusses a brief history and current status of the supercomputers in institutions for research and forecast of weather/climate. The main purpose of this study is to provide the preliminary information about supercomputers such as architecture of system and processor. Such information would be useful for meteorologists to understand the features and the preference of supercomputers in each institution.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Bulletin of the Korean Chemical Society
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• v.20 no.5
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• pp.525-527
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• 1999

Many types of chemical data tend to be characterized by many measured variables on each of a few observations. In this situation, target concentration can be predicted using multivariate statistical modeling. However, it is necessary to use a few variables considering size and cost of instrumentation, for an example, for development of a portable biomedical instrument. This study presents, with a spectral data set of total hemoglobin in whole blood, the possibility that modeling using only a few variables can improve predictability compared to modeling using all of the variables. Predictability from the model using three wavelengths selected from all possible regression method was improved, compared to the model using whole spectra (whole spectra: SEP = 0.4 g/dL, 3-wavelengths: SEP=0.3 g/dL). It appears that the proper selection of variables can be more effective than using whole spectra for determining the hemoglobin concentration in whole blood.