• 한국군사과학기술학회지
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• 제20권6호
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• pp.774-780
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• 2017

The techniques for driving trajectory calculation and driving trajectory distribution calculation are proposed to analyze the durability of ground vehicles effectively. To achieve this aim, the driving trajectory of a vehicle and the driving trajectory distribution of that are needed, in addition to road profile. The road profiles can be measured by a profilometer but a driving trajectory of a vehicle cannot be acquired effectively due to a large position error from a conventional GPS sensor. Therefore two techniques are proposed to reduce the position error of a vehicle and achieve the distribution of driving trajectory of that. The driving trajectory calculation technique produces relative positions by using the velocity, time and heading of a vehicle. The driving trajectory distribution calculation technique produces distributions of the driving trajectory by using axis transformation, estimating reference line, dividing sectors and plotting a histogram of the sectors. As a results of this study, we can achieve the considerably accurate driving trajectory and driving trajectory distribution of a vehicle.

• 한국군사과학기술학회지
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• 제19권1호
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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.

• 한국군사과학기술학회지
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• 제21권1호
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• pp.1-9
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• 2018

In this paper, we have proposed a prediction method for sabot-trajectory of projectile using high speed camera data analysis. Through analyzing trajectory of sabot with high speed camera data, we can extract its real velocity and acceleration including effects of friction force, pressure of flume, etc. Using these data, we suggest a prediction method for sabot-trajectory of projectile having variable acceleration, especially for minimum and maximum acceleration, by using interpolation method for velocity and acceleration data of sabot. Also we perform the projectile launching tests to achieve the trajectory of sabot in case of minimum and maximum thrust. Simulation results show that they are similar to real tests data, for example velocity, acceleration and the trajectory of sabot.

• 한국항공우주학회지
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• 제43권4호
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• pp.349-358
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• 2015

본 논문은 미래 항공교통관리(Air Traffic Management, ATM)의 핵심개념으로 받아들여지고 있는 궤적기반운용(Trajectory-based Operations, TBO)에 대한 기술동향을 담고 있다. ICAO(International Civil Aviation Organization)가 발표한 ASBU(Aviation System Block Upgrade)에 기술된 TBO 운용개념을 살펴보고, 미국과 유럽에서 각각 진행 되어온 궤적기반운용에 대한 운용개념 수립과 관련 연구개발 사례들을 통해 근 미래에 실현 가능한 운용개념 및 절차를 구체화하였다. 운용개념 정리를 통해 파악된 요소기술들(technical enablers)을 간단히 소개하며, 관련된 연구개발 진행 현황을 정리하였다.

• 한국군사과학기술학회지
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• 제21권5호
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• pp.572-582
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• 2018

In this paper, we have calculated a formular for sabot-trajectory of shipboard projectile including ship motion and generated trajectory-range function for analysing interference with structure of naval ship. We make formula to approximate the ship motion data of naval ship using optimization technique. Through this formula, we calculate the velocities and accelerations of sabot caused by ship motion(surge, sway, heave, roll, pitch, yaw) and then, we produce the formula about the trajectory of sabot including effects of ship motion in addition to previous study which had considered the effects of the pressure of flume, friction force, etc. To investigate interference with ship structures, we make the trajectory-range functions and then extract the nearest or farthest trajectory to ship structure. Through these data, we can conform whether the interference is happened or not.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.346-350
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• 2023

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.

• 아동학회지
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• 제28권5호
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• pp.127-141
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• 2007

The purpose of this research was to analyze the trajectory of child's academic attainment and the effect of family socioeconomic background on the trajectory. Data were part of the Korea Youth Panel Survey 2003-2005(Middle School 2) and were analyzed by Latent Growth Curve Modeling(LGM). The degree of child's academic attainment decreased over 3 years. Socioeconomic status variables that influenced academic trajectory were family poverty, parent's attainments in scholarship, and family structure. Findings from this study suggest that societal support for low socioeconomic status families is needed for improvement of academic attainment of their children.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.145-156
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• 2023

Trajectory of ballistic missile is defined by inherent flight dynamics, which decided range and maneuvering characteristics. It is crucial to predict range and maneuvering characteristics of ballistic missile in KAMD (Korea Air and Missile Defense) to minimize damage due to ballistic missile attacks, Nowadays, needs for applying AI(Artificial Intelligence) technologies are increasing due to rapid developments of DNN(Deep Neural Networks) technologies. To apply these DNN technologies amount of data are required for superviesed learning, but trajectory data of ballistic missiles is limited because of security issues. Trajectory data could be considered as multivariate time series including many variables. And augmentation in time series data is a developing area of research. In this paper, we tried to augment trajectory data of ballistic missiles using recently developed methods. We used TimeVAE(Time Variational AutoEncoder) method and TimeGAN(Time Generative Adversarial Networks) to synthesize missile trajectory data. We also compare the results of two methods and analyse for future works.

• 전기전자학회논문지
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• 제25권4호
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• pp.766-769
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• 2021

딥러닝을 포함한 머신러닝 기법을 기반으로 비행체의 궤적 설계, 제어, 최적화, 예측 등의 작업을 수행하기 위해서는 일정한 양 이상의 비행체 궤적 데이터를 필요로 한다. 그러나 다양한 이유(예를 들어 비행체 궤적 데이터셋 구축에 필요한 비용, 시간, 인력 등)로 일정한 양 이상의 비행체 궤적 데이터를 확보하기 어려운 경우가 존재한다. 이러한 경우 합성 데이터 생성이 머신러닝을 가능하게 하는 방법 중 하나가 될 수 있다. 본 논문에서는 이와 같은 가능성을 탐구하기 위하여 시계열 생성적 적대 신경망을 이용하여 비행체 궤적 합성 데이터를 생성하고 평가하였다. 또한 비행체의 상태를 인식하기 위한 비행체 궤적 예측 작업에서 합성 데이터의 활용 가능성을 탐구하기 위하여 다양한 ablation study(비교 실험)를 수행하였다. 본 논문에서 제시된 생성 평가 및 비교 실험 결과는 비행체 궤적 합성 데이터 생성 및 비행체 궤적 관련 작업에서 합성 데이터의 활용 가능성에 대한 연구를 수행하고자 하는 연구자들에게 실질적인 도움이 될 것으로 예상한다.