• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.335-343
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• 2023

Drone based Magnetic Anomaly Detection measure a magnetic anomaly signal from the ferromagnetic target on the ground. We conduct a magnetic anomaly detection with 9 ferromagnetic targets on the ground. By removing the magnetic field measured in the absence of ferromagnetic targets from the experimental value, the magnetic anomaly signal is clearly measured at an altitude of 100 m. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics. The simulation results are within the GPS error range of the experimental results.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.1-10
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• 2022

Defense fields widely operate unmanned systems to lower vulnerability and enhance combat effectiveness. In the navy, swarm unmanned surface vehicles(USVs) form a cluster within communication range, share situational awareness information among the USVs, and cooperate with them to conduct military missions. This paper proposes an interface system, i.e., Interface Adapter System(IAS), to achieve inter-USV and intra-USV interoperability. We focus on the mission planning subsystem(MPS) for interoperability, which is the core subsystem of the USV to decide courses of action such as automatic path generation and weapon assignments. The central role of the proposed system is to exchange interface data between MPSs and other subsystems in real-time. To this end, we analyzed the operational requirements of the MPS and identified interface messages. Then we developed the IAS using the distributed real-time middleware. As experiments, we conducted several integration tests at swarm USVs simulation environment and measured delay time and loss ratio of interface messages. We expect that the proposed IAS successfully provides bridge roles between the mission planning system and other subsystems.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.109-119
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• 2013

An unmanned ground vehicle(UGV) today plays a significant role in both civilian and military areas. Predominantly these systems are used to replace humans in hazardous situations. To take unmanned ground vehicles systems to the next level and increase their capabilities and the range of missions they are able to perform in the combat field, new technologies are needed in the area of command and control. For this reason, we present war field simulator based on information fusion technology to efficiently control UGV. In this paper, we present the war field simulator which is made of critical components, that is, simulation controller, virtual image viewer, and remote control device to efficiently control UGV in the future combat fields. In our information fusion technology, improved methods of target detection, recognition, and location are proposed. In addition, time reduction method of target detection is also proposed. In the consequence of the operation test, we expect that our war field simulator based on information fusion technology plays an important role in the future military operation significantly.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.353-359
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• 2021

The Army is constructing a training system using Miles equipment that applies the latest science and technology to carry out military training. The Miles training system is a system that uses Miles equipment to simulate the damage situation of combat personnel and equipment in the same way as an actual battlefield by conducting practiced maneuvers in the field. Through this, the training force can experience conditions similar to an actual battle. In particular, the training effects of the warriors participating in the training can be maximized by establishing an integrated system that utilizes cutting-edge science technologies, such as information communication and computer simulation. This study analyzed the effects of Miles training in the army using scientific techniques targeted at the mid-range Miles. In particular, the effect index for analyzing the training effect was derived from a literature survey and expert opinions. The weight of each effect index was calculated by applying the Swing method. The final training effect was calculated by combining the results of the survey from train-experienced people. The Miles training effect was 2.6 times more effective than previous training without using Miles, and the satisfaction rate with Miles training according to status was high through variance analysis, and the difference was statistically significant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.334-342
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• 2021

In Korea, the training is performed through independent environments without interoperability among L-V-C systems. In the L system, training for large units is limited due to civil complaints at the training grounds and road restrictions. The V system is insufficient in training related to tactical training, and the C system lacks practicality due to a lack of combat friction elements. To achieve synchronicity and integration training between upper and lower units, it is necessary to establish a system to ensure integrated training for each unit by interoperating the currently operating L, V, and C systems. The interoperability between the C-C system supports Korea-US Combined Exercise. On the other hand, the actual development of the training system through the interoperability of L, V, and C has not been made. Although efforts are being made to establish the L, V, and C system centering on the Army, the joint composite battlefield and LVC integrated architecture technology are not yet secured. Therefore, this paper proposes a new plan for the future training system by designing and implementing the LVC integrated architecture technology, which is the core technology that can build the L-V-C interoperability training system. In conclusion, a division-level L-V-C interoperability training system can be established in the future by securing the LVC integrated architecture technology.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.107-118
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• 2015

Many approaches to agent collaboration have been introduced in military war-games, and those approaches address methods for simulation entity (actor) collaboration within a team to achieve given goals. To meet fast-changing battlefield situations, an actor must be loosely coupled with their tasks and be able to take over the role of other actors if necessary to reflect role handovers occurring in real combat. Achieving these requirements allows the transfer of tasks assigned one actor to another actor in circumstances when that actor cannot execute its assigned role, such as when destroyed in action. Tight coupling between an actor and its tasks can prevent role handover in fast-changing situations. Unfortunately, existing approaches and war-game strictly assign tasks to actors during design, therefore they prevent the loose coupling. To overcome these shortcomings, our Role-based Command Hierarchy (ROCH) model dynamically assigns roles to actors based on their situation at runtime. In the model, "Role" separates actors from their tasks. In this paper, we implement the ROCH model as a component that uses a publish-subscribe pattern to handle the link between an actor and the roles of its subordinates (other actors).

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.53-61
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• 2010

In wireless sensor networks(WSNs) individual sensor nodes are subject to security compromises. An adversary can physically capture sensor nodes and obtain the security information. And the adversary injects false reports into the network using compromised nodes. If undetected, these false reports are forwarded to the base station. False reports injection attacks can not only result in false alarms but also depletion of the limited amount of energy in battery powered sensor nodes. To combat these false reports injection attacks, several filtering schemes have been proposed. The statistical en-routing filtering(SEF) scheme can detect and drop false reports during the forwarding process. In SEF, The number of the message authentication codes(threshold) is important for detecting false reports and saving energy. In this paper, we propose a dynamic threshold determination method for energy efficient SEF using fuzzy-logic in wireless sensor networks. The proposed method consider false reports rate and the number of compromised partitions. If low rate of false reports in the networks, the threshold should low. If high rate of false reports in networks, the threshold should high. We evaluated the proposed method’s performance via simulation.