• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.67-80
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• 2022

The missile early-warning satellite systems have been developed and upgraded by some space-developed nations, under the inevitable trend that the space is more strongly considered as another battle field than before. As the key function of such a satellite-based early warning system, the prediction algorithm of the missile flight trajectory is studied in the paper. In particular, the evolution computation, receiving broad attention in the artificial intelligence area, is applied to the proposed prediction method so that the global optimum-like solution is found avoiding disadvantage of the previous non-linear optimization search tools. Moreover, using the prediction simulator of the launch vehicle flight trajectory which is newly developed in C# and Python, the paper verifies the performance and the feature of the proposed algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.2
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• pp.1-8
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• 2014

This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.4
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• pp.27-36
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• 2019

An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1244-1253
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• 2021

In this paper, we propose a method for predicting in advance whether pedestrians will enter the hazardous area after the current time using the pedestrian trajectory prediction method and an efficient simplification method of the trajectory prediction network. In addition, we propose a method to apply KD(Knowledge Distillation) to a small network for real-time operation in an embedded environment. Using the correlation between predicted future paths and hazard zones, we determined whether to enter or not, and applied efficient KD when learning small networks to minimize performance degradation. Experimentally, it was confirmed that the model applied with the simplification method proposed improved the speed by 37.49% compared to the existing model, but led to a slight decrease in accuracy. As a result of learning a small network with an initial accuracy of 91.43% using KD, It was confirmed that it has improved accuracy of 94.76%.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.160-160
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• 2008

In practical etching process, etch ant is sprayed on the metal-deposited panel through nozzles collectively connected to the manifold and that panel is usually composed of many PCB(printed circuit board)'s. The etching uniformity, the difference between individual PCB's on the same panel, has become one of most important features of etching process. In this paper, the prediction of nozzle trajectory has been performed by the combination of algebraic formula and numerical simulation. With the pre-determined geometrical factors of nozzle distribution, the trajectories of individual nozzles were predicted with the change of process operational factors such as panel speed, nozzle swing frequency and so on. As results, two dimensional distribution of impulsive force of etchant spray which could be considered as a key factor determining the etching performance have been successfully obtained. Though only qualitative prediction of etching uniformity have been predicted by the process developed in this study, the expansion to the quantitative prediction of etching uniformity is expected to be apparent by this study.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.131-140
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• 2020

The characteristics of ballistic missiles are changing rapidly but studies have mostly focused on fragmentary flight trajectory analysis estimating the changing characteristics of some types, while there is a lack of research on comprehensive and efficient ballistic search, detection and prediction for missiles including the new types that have been gaining attention lately. This paper analyzes the flight trajectory characteristics of ballistic missiles at various ranges considering flight path angle adjustment, specific impulse and drag force with altitude based on the optimized equations of motion reflecting the parameters of North Korea's general and new types of ballistic missiles. The flight trajectory characteristics of representative ranges for each ballistic missile were analyzed by adjusting the flight path angle in the minimum energy method, lofted method, and depressed method. In addition, High value target can attacked by ballistic missiles considering flight path angle adjustment at various points. It's expected to be used to Threat Evaluation and Weapon Allocation, and deployment of defense systems by interpreting the analysis of the latest Iskander-class ballistic missiles and the new multiple rocket launcher.

• Journal of IKEEE
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• v.25 no.3
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• pp.540-543
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• 2021

In this paper, we present an experimental comparative study of recurrent neural network based techniques for trajectory prediction of flight vehicles. We defined and investigated various relationships between input and output under the same experimental setup. In particular, we proposed a relationship based on the relative positions of flight vehicles. Furthermore, we conducted an ablation study on the network architectures and hyperparameters. We believe that this comprehensive comparative study serves as a reference point and guide for developers in choosing an appropriate recurrent neural network based techniques for building (flight) vehicle trajectory prediction systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.375-384
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• 2010

Trajectory modeling is foundational for 4D-Route modeling, conflict detection and air traffic flow management. This paper proposes a novel algorithm based Vincenty's fomulas for trajectory calculation, combined with the Dijkstra algorithm and Vincenty's formulas. Using flight plan simulations our experimental results show that our method of En-route trajectory calculation exhibits much improved performance in accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1047-1055
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• 2009

Since nuclear and chemical materials could damage people and disturb battlefield missions in a wide region, nuclear/chemical reconnaissance systems utilizing multiple mobile robots are highly desirable for rapid and safe reconnaissance. In this paper, we design a nuclear/chemical reconnaissance system including mobile robots. Also we propose time-efficient trajectory planning algorithms using grid coverage and contour finding methods for reconnaissance operation. For grid coverage, we performed in analysis on time consumption for various trajectory patterns generated by straight lines and arcs. We proposed BCF (Bounded Contour Finding) and BCFEP (Bounded Contour Finding with Ellipse Prediction) algorithms for contour finding. With these grid coverage and contour finding algorithms, we suggest trajectory planning algorithms for single, two or four mobile robots. Various simulations reveal that the proposed algorithms improve time-efficiency in nuclear/chemical reconnaissance missions in the given area. Also we conduct basic experiments using a commercial mobile robot and verify the time efficiency of the proposed contour finding algorithms.