• Journal of The Korean Institute of Defense Technology
• /
• v.6 no.1
• /
• pp.7-12
• /
• 2024

In March 2023, the Ministry of National Defense highlighted the Synthetic Training Environment (STE) platform for practical training of combatants between training exercises in its Defense Innovation 4.0 Basic Plan. However, there are many issues to be resolved, such as the use of different simulators and terrain information systems for each military. Therefore, this study examined the cases of the U.S. Army, Air Force, and Navy, each of which has an advanced synthetic training environment, to derive suggestions for the ROK military.

• Journal of the Korea Society for Simulation
• /
• v.31 no.2
• /
• pp.21-32
• /
• 2022

The ROK Navy is seeking to secure an aircraft carrier(CVX) to take responsibility for the maritime security of the Republic of Korea. In order for the CVX to complete the mission given to it, the crew must be able to operate the CVX perfectly, and for this purpose, the operating skills of the CVX crew result from constant training. Therefore, this paper proposes an On-board Training System(OBTS) so that the best training can always be performed even on ship. CVX OBTS should be built in the form of a thorough simulator based on a Synthetic Training Environment(STE) so that it can be optimally applied to ship and provide the best training environment to the crew. In order to satisfy the various training requirements and implementation conditions of the CVX, this paper proposes a plan to consist of Embedded Training System(ETS), VR training system, AR maintenance system, MR training system, MR metaverse training system, and realistic simulator training system.

• 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.

• 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.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.139-149
• /
• 2019

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.893-903
• /
• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.15 no.1
• /
• pp.36-45
• /
• 2012

In this thesis, we propose an integrated simulation method of virtual tank simulators and an entity-based constructive simulation model for small unit tactical training. To do this, we first identify requirements for virtual-constructive integrated simulation in a synthetic environment. We then propose a virtual and constructive interoperation method where individual combat entities of virtual-constructive models are interacting with each others. We develop a method of aggregating individual combat entities into a larger combat unit and disaggregating an unit into entities from time to time. We also present a way of sharing synthetic environment information between the models. Finally, we suggest that for more effective interoperability, virtual and constructive models should be developed by using common combat object models. The proposed interoperation method can be extended to further live-virtual-constructive models.

• Journal of the Korea Society for Simulation
• /
• v.25 no.3
• /
• pp.29-39
• /
• 2016

In this research, political suggestions for Korean navy have been drawn through examination of FST (Fleet Synthetic Training), simulation based training system. We, first, reviewed overall concepts of FST and its key components. We then examined the current status of Korean navy in terms of preparation of FST system. To draw objective strategy and plan, indepth SWOT analysis from the stand point of our internal capability and FST aspect environment has been made. Gradual settlement on the current integrated tactical training in-port should be the good solution for Korean Navy.

• 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 Information Technology Services
• /
• v.20 no.1
• /
• pp.67-79
• /
• 2021

The advent of the 4th Industrial Revolution is also causing many changes in defense operations. Defense reform and the fourth industrial revolution promoted smart defense innovation, and attempts are being made to incorporate cutting-edge science and technology into various fields such as weapons systems and defense operations. Education and training is one of the areas in which information and intelligence are urgently needed in the spirit of defense operations. Due to the nature of defense education and training, which aims to fight against the enemy, there is no emphasis on psychological training in the field rather than informatization, but in developed countries with various experiences of modern warfare, investment and vitalization of education and training are vital. Through this, efforts are being made to foster soldiers with problem-solving skills in uncertain battlefields. The informatization and intelligence of defense education and training is no longer a matter that can be delayed, and the innovation of education and training using cutting-edge science and technology can be said to be an age-old task to improve the results of education and training in the fourth industrial revolution. The purpose of this is because the application of related technologies is not the goal itself as the 4th Industrial Revolution arrives, but it has been made possible through the rapid advancement of science and technology that has made it difficult to realize education and training, even though it has long been desired. Ultimately, education and training data will be integrated and artificial intelligence-based intelligent learning systems will maximize the performance of education and training, thereby improving the combat readiness.