• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.49-64
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• 2021

M&S techniques are widely used as scientific means to systematically develop response plans to chemical and biological (CB) hazards. However, while the theoretical area of hazard dispersion modeling has achieved remarkable practical results, the operational analysis area to simulate CB hazard response plans is still in an early stage. This paper presents a model to simulate CB hazard response plans such as detection, protection, and decontamination. First, we present a possible way to display high-fidelity hazard dispersion in a combat simulation model, taking into account weather and terrain conditions. We then develop an improved vulnerability model of the combat simulation model, in order to simulate CB damage of combat simulation entities based on other casualty prediction techniques. In addition, we implement tactical behavior task models that simulate CB hazard response plans such as detection, reconnaissance, protection, and decontamination. Finally, we explore its feasibility by analyzing contamination detection effects by distributed CB detectors and decontamination effects according to the size of the {contaminated, decontamination} unit. We expect that the proposed model will be partially utilized in disaster prevention and simulation training area as well as analysis of combat effectiveness analysis of CB protection system and its operational concepts in the military area.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.23-31
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• 2023

Software development involves a series of stages, including requirements analysis, design, implementation, unit testing, and integration testing, similar to those used in the system engineering process. This study utilized MathWorks' model-based design platform to develop multi-function radar software and evaluated its feasibility and efficiency. Because the development of conventional radar software is performed by a unit algorithm rather than in an integrated form, it requires additional efforts to manage the integrated software, such as requirement analysis and integrated testing. The mode-based platform applied in this paper provides an integrated development environment for requirements analysis and allocation, algorithm development through simulation, automatic code generation for deployment, and integrated requirements testing, and result management. With the platform, we developed multi-level models of the multi-function radar software, verified them using test harnesses, managed requirements, and transformed them into hardware deployable language using the auto code generation tool. We expect this Model-based integrated development to reduce errors from miscommunication or other human factors and save on the development schedule and cost.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.349-356
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• 2014

In the underwater communication, the performance of system is reduced because of the inter-symbol interference occur by the multi-path. In the recent years, to deal with poor channel environment and improve the throughput, the efficient concatenated structure of equalization, channel codes and Space Time Codes has been studied as MIMO system in the underwater communication. Space Time Codes include Space Time Block Codes and Space Time Trellis Codes in underwater communication. Space Time Trellis Codes are optimum for equalization and channel codes among the Space Time Codes to apply in the MIMO environment. Therefore, in this paper, turbo pi codes are used for the outer code to efficiently transmit in the multi-path channel environment. The inner codes consist of Space Time Trellis Codes with transmission diversity and coding gain in the MIMO system. And Zero Forcing method is used to remove inter-symbol interference. Finally, the performance of this model is simulated in the underwater channel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.974-985
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• 2016

Riverbed Modeler, which is a commercial packet-level discrete event simulator is used to model, design, and simulate complicated communication protocols and large-scale network. Riverbed Modeler got credit for its reliability in field of network simulation. In the MANET simulation environment using Riverbed Modeler, it is very complicated to add a new routing protocol into existing architecture of routing protocols because it is required lots of modifications of protocol recognition. In this paper, we propose Routing Adding Framework which can reduce errors or mistakes during modifying the existing routing support architecture. Routing Adding Framework is provided as a adapter API for protocol recognition. and it is only minimum modifications for protocol identifiers when a new routing protocol is added to the child process of manet_mgr process which manages routing protocols for IP layer. With Routing Adding Framework, we can reduce less than half modification. Then, we shows an example of implementation of a hybrid routing protocol AntHocNet using Routing Adding Framework, and we verify its design and application of the Routing Adding Framework by obtaining simulation result with similar result given by AntHocNet.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.233-238
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• 2020

Although the interest in NBR has been increasing due to the recent developments of the aerospace sector, there are few reports on HNBR's aeronautical oil, particularly evaluations of the accelerated life of harsh factors. In this study, the tensile strength was adopted as a performance evaluation factor to evaluate the accelerated life of HNBR used in the aviation field. The accelerated stress factor affecting the performance-aging characteristics was defined as temperature. The acceleration stress factor was determined to be temperature, and the result of measuring the tensile strength change over time. The sample for the acceleration condition was taken out of the oven for a certain period and left at room temperature for 24 hours. The dumbbell type 3 specimens were manufactured according to the standard specified in KS M 6518 and were measured the tensile strength, a factor in accelerated life evaluations. The activation energy was 0.895, and the shape parameter was 1.152 using the Arrhenius model. The characteristic life obtained from the tensile strength of the HNBR specimen immersed in aviation oil at 20℃ was 272,256 hours; the average life was 258,965 hours, and the B10 life was 38,624 hours.