• Journal of Korean Society on Water Environment
• /
• v.26 no.3
• /
• pp.532-539
• /
• 2010

The synthetic unit hydrograph is developed and verified using Nash model and characteristic velocities considering geomorphological dispersion in this present study. Application watersheds are selected 5 subwatersheds of Bocheong basin. The mean and variance of hillslope and stream path length are estimated in each watershed with GIS. Characteristic velocities are calculated using estimated path lengths and moment characteristics of rainfall-runoff data. Characteristic velocities of random devised 7 ungauged watersheds are estimated through regional analysis of chracteristic velocities in guaged watershed. And Nash model parameters and IUH are derived using characteristic velocities and path length in the gauged and ungauged watershed. The result to compare of IUH about gauged watershed and random devised ungauged watershed in application watershed presents coherently hydrologic response characteristics that peak discharge is reduced and peak time is extended. In conclusion, Developed synthetic unit hydrograph in this study expects that it is useful method to estimate runoff discharge for managing of water pollution in ungauged watershed.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.5
• /
• pp.476-486
• /
• 2022

Synthetic dynamic infrared image generation from the given virtual environment is being the primary goal to simulate the output of the infra-red(IR) camera installed on a vehicle to evaluate the control algorithm for various search & reconnaissance missions. Due to the difficulty to obtain actual IR data in complex environments, Artificial intelligence(AI) has been used recently in the field of image data generation. In this paper, CycleGAN technique is applied to obtain a more realistic synthetic IR image. We added the Structural Similarity Index Measure(SSIM) loss function to the L1 loss function to generate a more realistic synthetic IR image when the CycleGAN image is generated. From the simulation, it is applicable to the guided-missile flight simulation tests by using the synthetic infrared image generated by the proposed technique.

• Journal of the Korea Society for Simulation
• /
• v.19 no.4
• /
• pp.235-247
• /
• 2010

An environmental factor is very important in a war game model supporting military training. Most war game models in Korean armed forces apply the same weather conditions to all operation areas. As a result, it fails to derive a high-fidelity simulation result. For this reason this study attempts to develop factor techniques for a high-fidelity war game that can apply distributed synthetic atmospheric environment modeling data to a military simulation. The major developed factor technology of this study applies regional distributed precipitation data to the 2D-GIS based Simplified Detection Probability Model(SDPM) that was developed for this study. By doing this, this study shows that diversely distributed local weather conditions can be applied to a military simulation depending on the model resolution from theater level to engineering level, on the use from training model to analytical model, and on the description level from corps level to battalion level.

• Journal of Astronomy and Space Sciences
• /
• v.41 no.2
• /
• pp.61-78
• /
• 2024

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2003.11a
• /
• pp.43-50
• /
• 2003

시뮬레이션의 궁극적인 목표인 분산 시뮬레이션은 Virtual Simulation과 Constructive Simulation 이외에 Live Simulation이 동시에 참여하여 동일한 환경 내에서 상호작용 하는 것을 추구하고 있다. 따라서 서로 다른 목적과 기술로 제작된 시뮬레이션은 통합된 환경 내에서 사실적으로 상호작용 해야만 할 것이다. 통합 환경(Synthetic Environment)은 시각적 영상을 표현하기 위한 Visual Database 이상의 의미를 내포하고 있다. 상호운용성(Interoperability)은 타 Model 및 Simulation에서 부터 제공되는 기능을 사용하거나 기능을 제공할 수 있는 유용성(Availability)을 뜻하기 때문에 표준화된 통합 환경의 사용 및 교환을 요구한다. 1980년대 중반, 미 국방성은 Image Generator 제작 회사마다 독자적인 데이터베이스 포맷을 사용함으로 인해 시뮬레이터 데이터베이스의 중복성이 야기되고 있다는 것을 지적하고 통합 환경 데이터 교환 문제를 해결하기 위한 최초의 관련 연구를 수행하였다. Virtual Simulation에서 막대한 비용이 소모되는 시뮬레이터 데이터베이스의 제작 및 유지 보수에 들어가는 이중적인 비용 절감에 대한 관심은 최근 SEDRIS에 이르러 표준 교환에 대한 명세의 필요성이 보다 구체화되어 지구상에 존재하는 각종 주변 환경을 기술하기 위한 통합 환경표현 및 교환의 표준화가 진행되고 있다. 국내에서는 문화콘텐츠진흥원, 한국게임산업개발원, 한국전자통신연구원, 국방과학연구소, 서울대, 아주대, 세종대 등을 중심으로 연구가 진행 중이며, 그중 세종대학교는 SEDRIS 데이터로 사용될 원본 데이터의 충실도와 신뢰도를 향상시킬 수 있는 방법을 연구하고 있다. SEDRIS는 그 응용가능성이 무궁무진하여 향후 인공적인 가상환경을 대표하는 용어로 사용될 것이며, 공개된 표준을 통한 소스 데이터의 공유로 상호운용성을 위한 초석을 제공하여 궁극적으로는 비용 절감 효과가 있을 것으로 전망된다.

• The Journal of the Acoustical Society of Korea
• /
• v.25 no.4
• /
• pp.172-177
• /
• 2006

Various research and development on Synthetic Aperture Sonar technique is under way to enhance bearing resolution of a SONAR system. In this paper, we estimated perturbed array shapes, and compensated distortion by using estimated away shapes and synthesized arrays in aperture domain such as an ETAM technique. As experimental data, we used the one obtained from towed array in neighboring waters of the Korean peninsula. Through simulation on data where tow-ship speed is maintained at a constant level, we confirmed that synthesis effect of increasing SNR and narrowed beam width of main lobe was consistently demonstrated for about 1 minute when coherence of target signal was maintained. Also, we showed that the synthesis effect with respect to time was constantly maintained.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2005.04a
• /
• pp.348-351
• /
• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.6
• /
• pp.428-436
• /
• 2004

Synthetic Aperture Sonar is a technique of extending Physically limited length of an array by signal processing to enhance bearing resolution of a system. The previous techniques estimate most or away shapes as linear. so when towed array shapes are distorted. this can create a deviation from actual situation. In this paper. we estimated perturbed away shapes. and compensated distortion by using estimated array shapes and synthesized arrays in aperture domain. As experimental data, we used the one obtained from towed array in neighboring waters of the Korean peninsula. We extended array by compensating differences in time and spatial position between overlapped subarrays by using SAS techniques. In simulation results. we confirmed that the bearing resolution was enhanced.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.53-60
• /
• 2003

The M&S community needs an environmental data representation and interchange mechanism which not only satisfies the requirements of today's systems, but can be extended to meet future data sharing needs. This mechanism must allow for the standard representation of, and access to data. It must support databases containing integrated terrain, ocean, atmosphere, and space data. The SEDRIS provides environment data users and producers with a clearly defined interchange specification. In this paper I present the method to extract the data attributes contained in synthetic environment domain using SEDRIS technology and API.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.122-130
• /
• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.