• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.117-119
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• 2012

기존의 탄도방정식[2]에서 여러 조건을 제시하여 간략화된 대공화기 탄도방정식을 얻는다. 대공화기의 탄도궤적이므로 양력계수가 들어간 항의 값이 충분히 작다는 가정을 하였다. 또한 속도의 크기를 시간불변이라는 가정을 하였다. 이 탄도방정식은 기존의 방정식[1]에 비하여 밀도, 풍속, 항력계수 및 탄도계수가 식에 나타나 있어 일반적인 탄도방정식으로 이용가능하고 또한 미분방정식의 해를 구할 필요가 없다. 모의실험을 통하여 제시된 탄도방정식을 이용하여 풍속이 들어간 탄도궤적을 구한다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.279-282
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• 2011

Projectile can be damaged during the storage and handling. Maximum range calculation of the ammunition was performed on the assumption that each projectiles have 1.5mm/3.3mm axisymetric dent on the surface. Drag coefficient for trajectory calculation was delivered from CFD using commercial software FLUENT. In the result of CFD, damaged projectiles those have 1.5mm/3.3mm axisymetric dent have similar drag coefficient compare with normal projectile in the region of subsonic. But, in supersonic region, drag coefficient was increased 3%, 9% each in average. In the result of trajectory calculation, Maximum rage was decreased 1%, 3% each.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.8
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• pp.531-540
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• 2022

In this paper, we present an algorithm that identifies artillery type and rapidly predicts the impact point based on the IMM filter. The ballistic trajectory equation is used as a system model, and three models with different ballistic coefficient values are used. Acceleration was divided into three components of gravity, air resistance, and lift. And lift acceleration was added as a new state variable. The kinematic condition that the velocity vector and lift acceleration are perpendicular was used as a pseudo-measurement value. The impact point was predicted based on the state variable estimated through the IMM filter and the ballistic coefficient of the model with the highest mode probability. Instead of the commonly used Runge-Kutta numerical integration for impact point prediction, a semi-analytic method was used to predict impact point with a small amount of calculation. Finally, a state variable initialization method using the least-square method was proposed. An integrated algorithm including artillery type identification, impact point prediction and initialization was presented, and the validity of the proposed method was verified through simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.95-104
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• 2016

To make a firing table of artillery with trajectory simulation, a precise trajectory model which corresponds with real firing test is required. Recent 4-DOF modified point mass trajectory model is considered accurate as a theoretical model, but fitting coefficients are used in calculation to match with real firing test results. In this paper, modified point mass trajectory model is presented and method of setting ballistic coefficient is introduced by applying optimization algorithms. After comparing two different algorithms, Particle Swarm Optimization and Covariance Matrix Adaptation - Evolutionary Strategy, we found that using CMA-ES algorithm gives fine optimization result. This fitting coefficient setting method can be used to make trajectory simulation which is required for development of new projectiles in the future.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.1
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• pp.98-103
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• 2017

In this paper, determination method for drag force and drag coefficient from results of firing test is described. The drag force and drag coefficient are determined through inverse operation of 2-dimensional projectile equation of motion. Determination method was verified by comparing analytical drag coefficient with data from flight test. Analysis of drag coefficient and drag reduction was performed with the data of flight test using artillery projectiles with base bleed unit.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.265-274
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• 2014

A recent air defense missile system(ADMS) is required to have a capability to intercept super-high speed targets such as tactical ballistic missiles(TBMs) by performing engagement control efficiently. The air defense missile system should be ready to engage the TBMs as soon as the ADMS detects TBMs because falling velocity of TBM is very high and remaining time interval to engage TBM is very short. As a result, the ADMS has to predict the trajectories of TBMs accurately with estimated states of dynamics to generate predicted intercept point(PIP). In addition, it is needed to engage TBMs accurately via transmitting the obtained PIP data to the corresponding intercept missiles. In this paper, an analysis about the relationship between ballistic coefficient and PIP accuracy which is depending on geodetic height of the first detection of TBM is included and an issue about effective engagement control for the TBM is considered.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.520-526
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• 2012

The appearance of the negative differential pressure(NDP), in which the shot base pressure is higher than the breech pressure, indicates that a potential damage on the gun system is increased. In order to safeguard the gun system, the igniter must be designed to minimize the NDP during the firing process. From this reason, the effect of igniter's burning rate on the NDP of the interior ballistics has been investigated through the numerical simulations. The NDP has been increased with increment of the coefficient in the burning rate of the igniter. A sudden change of the chamber pressure has been shown in case of using a singular coefficient.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.149-159
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• 2021

This paper represents a reentry time prediction analysis of CZ-5B rocket-body in China, subject to analysis of the Inter-Agency Space Debris Coordination Committee Reentry (IADC) reentry test campaign conducted in May 2021. Predicting the reentry of space objects is difficult to accurately predict due to the lack of accurate physical information about target, and uncertainty in atmospheric density. Therefore, IADC conducts annual re-entry campaigns to verify analysis techniques by each agency, and the Korea Aerospace Research Institute has also participated in them since 2015. Ballistic coefficient estimation method proposed to predict target reentry time and the result confirmed the difference of 73 seconds, which confirms the accuracy of the proposed method.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.