• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.

• Journal of Food Hygiene and Safety
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• v.33 no.3
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• pp.157-161
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• 2018

The objective of this study was to develop software to predict the kinetic behavior and the probability of foodborne bacterial growth on processed meat products. It is designed for rapid application by non-specialists in predictive microbiology. The software, named Foodborne bacteria Animal product Modeling Equipment (FAME), was developed using Javascript and HTML. FAME consists of a kinetic model and a probabilistic model, and it can be used to predict bacterial growth pattern and probability. In addition, validation and editing of model equation are available in FAME. The data used by the software were constructed with 5,400 frankfurter samples for the kinetic model and 345,600 samples for the probabilistic model using a variety of combinations including atmospheric conditions, temperature, NaCl concentrations and $NaNO_2$ concentrations. Using FAME, users can select the concentrations of NaCl and $NaNO_2$ meat products as well as storage conditions (atmosphere and temperature). The software displays bacterial growth patterns and growth probabilities, which facilitate the determination of optimal safety conditions for meat products. FAME is useful in predicting bacterial kinetic behavior and growth probability, especially for quick application, and is designed for use by non-specialists in predictive microbiology.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1352-1361
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• 2000

We obtain a micromechanics-based Helmholtz free energy and then in the framework of irreversible thermodynamics, a kinetic relation, a martensitic nucleation criterion and the reorientation criterion of martensitic variants are obtained. These relations are valid for a three-dimensional proportional and non-proportional loadings and for a combination of mechanical and thermal loading. From the simulated pseudoelastic stress-strain relation of a single crystal with loading rate effect, polycrystalline behavior in case of proportional and non-proportional loading is predicted by a homogenization method. The obtained results are compared quantitatively with experimental results.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.250-257
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• 2000

A Helmholtz free energy for a martensitic transformation of a single crystalline shape-memory alloy is obtained by a micromechanical approach, 24 variants of the single crystal are taken into account. In the framework of irreversible thermodynamics, a kinetic relation, a martensitic nucleation criterion and the reorientation criterion of martensitic variants are obtained. These relations are valid for a three-dimensional proportional or non-proportional mechanical loading or a combination of mechanical and thermal loading. Reorientation behavior of a single crystalline shape-memory alloy CuZnAl is simulated. When a tensile load is applied to a thermally-induced martensite, 24 self-accommodated martensitic variants are reoriented to the most favorable variant. In the following unloading, the most favorable variant in the tensile load is reoriented to the most favorable variant in this loading condition.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1127-1134
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• 2002

The effects of pressure and temperature on the autoignition of propane and n-butane blends were investigated using a rapid compression machine (RCM) , which is widely used to examine the autoignition characteristics. The RCM was designed to be capable of varying the compression ratio between 5 and 20 and minimize the vortex formation on the cylinder wall using a wedge-shaped crevice. The initial temperature and pressure of the compressed gas were varied in range of 720∼900 K and 1.6∼ 1.8 MPa, respectively, by adjusting the ratio of the specific heat of the mixture by altering the ratio of the non-reactive components (N$_2$, Ar) under a constant effective equivalence ratio (ø$\_$f/= 1.0) The gas temperature after the compression stroke could be obtained from the measured time-pressure record. The results showed a two-stage ignition delay and a Negative Temperature Coefficient (NTC) behavior which were the unique characteristic of the alkane series fuels. As the propane concentration in the blend were increased from 20% and 40% propane, the autoignition delay time increased by approximately 41 % and 55% at 750 K. Numerical reduced kinetic modeling was performed using the Shell model, which introduced some important chemical ideas, represented by the generic species. Several rate coefficients were calibrated based on the experimental results to establish an autoignition model of the propane and n-butane blends. These coefficients can be used to predict the autoignition characteristics in LPG fueled Sl engines.