• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.8
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• pp.558-565
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• 2019

A lot of studies on the survivability of missiles have been widely studied, since the technology of modern warships equipped with state-of-the-art defense systems has been improved. The survivability of missiles can be improved by attacking a target simultaneously using multiple missiles. For this reason, impact time control guidance (ITCG) laws have been widely studied. This paper deals with the effect of time-to-go estimate to ITCG laws. In this paper, two kinds of time-to-go estimate are first introduced in two-dimensional and three-dimensional environment and then ITCG laws are derived using the time-to-go estimate. Numerical simulations are performed to analyze the performance of the designed ITCG laws and the effect of time-to-go estimate is discussed.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.155-164
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• 2003

현재 국내에서는 교통사고 재현 방법이 노면 흔적물에 의존하여 이루어지고 있고, 노면 흔적물이 없는 경우 교통사고 재현은 불가능하게 된다. 이러한 단점을 보완하기 위해 외국에서는 충돌모델을 이용한 교통사고 재현프로그램을 활용하고 있는 추세이다. 현재 우리나라에서 가장 많이 사용하는 교통사고 재현 프로그램 PC-CRASH를 활용하기 위해서는 각 사고마다 알맞은 충돌특성 인자 값을 사용자가 직접 입력하여 사용하여야 하나, 이를 활용할 수 있는 활용자료가 부족한 실정이다. 본 연구는 국내에서 실제 발생된 교통사고 사례에 대해 교통사고 재현 프로그램인 PC-CRASH와 충돌에 영향을 미치는 충돌 인자들로 교통사고를 재현하였고, 차량의 최종위치간 거리와 자세에 어떠한 영향을 미치는가를 알아보았다. 또한 실제 역 해석을 하기 위해 차량의 최종위치와 자세만으로 각각의 사례에 적절한 인자값을 추정할 수 있는 회귀식을 구성하였고, 통계적으로 신뢰성을 검증하였다. 사고 재현에 필요한 주요 충돌인자들의 초기치 설정시 추정식을 이용할 경우, 사고 재현 프로그램 활용 시, 시간 단축 효과를 프로그램 내부에 있는 유전자 알고리즘의 반복횟수로 추정식의 통계적 검증을 하였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.616-619
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• 2012

When multiple Tags transmit their IDs to the Reader, tag identification time is delayed due to collisions. Therefore, to reduce the reader's identification time, an efficient anti-collision technology is needed. In this paper, a new anti-collision method is proposed. The method estimates the number of tags and allocates proper number of slots based on the DFSA. The performance of proposed method is compared with existing methods through extensive simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.248-253
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• 2011

Collision avoidance algorithm of vessels have been studied to avoid collision and grounding of a vessel due to human error. In this paper, We propose a collision avoidance algorithm using bayesian estimation theory for safety sailing and reduced risk of collision accident. We calculate collision risk for efficient collision avoidance using bayesian algorithm and determined the safest and most effective collision risk is predicted by using re-planned with re-evaluated collision risk in the future(t=t'). Others ship position is assumed to be informed from AIS. Experimental results show that we estimate the safest and most effective collision risk.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.202-202
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• 2021

최근 국내·외에서는 산지에서 발생하는 토사재해를 관리하기 위하여 유사이송해석에 필수적인 요소인 소류사량 추정 방안에 대한 연구가 활발히 진행되고 있다. 음향센서가 내장되어 있는 파이프 하이드로폰은 소류사 입자가 충돌 또는 통과하면서 발생하는 음향을 계측하여 소류사량을 간접적으로 계측하는 방법이다. 하이드로폰에 계측된 음향신호는 신호의 정점을 연결한 포락선 신호로 변환되며, 증폭기에 의해 다양한 배율의 신호로 증폭된다. 이렇게 증폭되어진 여러 배율의 신호와 원시신호는 데이터로거에 의해 지속적으로 기록되고, 가공처리하며 검출된 음향 펄스 수와 특정 시간간격의 음압적분치를 이용하여 소류사량을 추정하게 된다. 본 연구에서는 하이드로폰에 계측되는 소류사의 충돌음으로부터 정량적인 소류사량을 추정하기 위한 실험적 연구를 수행하였다. 단일 입경 6종류와 유속 3종류 및 소류사량 5단계로 변화를 주었으며 디노이징 필터를 통해 원시신호의 노이즈를 제거함으로써, 소류사량 추정률의 성능을 높이고자 하였다. 원시신호와 디노이징 음향신호를 비교·분석한 결과 제안된 방법이 기존 원시신호 보다 높은 소류사량 추정률을 보이는 것으로 나타났으며, 단일 입경 연속공급실험을 수행하여 소류사량 추정 가능성을 확인하였다. 향후 혼합 입경 소류사량 추정 실험 및 다양한 입경을 활용한다면 높은 소류사량 추정률을 얻을 것으로 기대된다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.7
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• pp.633-639
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• 2007

This paper presents a new homing guidance law based on well-known BPN to achieve an impact time constraint as well as an impact angle constraint. The guidance commands are synthesized by introducing an additional command to control impact-time. The structure of the additional command has a BPN-based loop multiplied by time-varying gains being proportional to the time difference between the required time-to-go and the estimated time-to-go by BPN. Moreover, the proposed homing loop converges to BPN as the time-to-go error is reduced. The performance of the proposed guidance law is evaluated by the computer simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.259-260
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• 2012

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1619-1624
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• 2012

If multiple Tags in the RFID System transmit their IDs to the Reader at the same time, tag identification time is delayed due to collisions. Therefore, to reduce the reader's identification time, an efficient anti-collision technology is needed. In this paper, we propose a hybrid anti-collision method based on the QT and DFSA. Then, the performances of proposed method are compared with the existing method through extensive simulations.