• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.31-37
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• 2007

This paper presents a ballistic lead-computation method which utilizes automatic video tracking, tracking assistance and roll uncoupling. The method is able to improve the firing accuracy of army fighting vehicles such as main battle tanks. In the experiment, the efficiency of the proposed method is evaluated by an error analysis in real operating environment. The proposed method has been applied to the fire control system of a military vehicle and proved through the development test of the vehicle.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.1099-1105
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• 2010

The purpose of this paper is to propose a methodology that can estimate optimal requirement of attack helicopter Korea army will be operating in future. For estimating optimal requirement, attack helicopter's operation concept, performance, battlefield environment and enemy threat are considered. We use a wargame model, AAsim(Army Aviation simulation), as a analytic simulation model which is used to analyze DOTMLPF and operation in army aviation field. In this paper, we conduct battle experiment for anti armored corps operation which reflects attack helicopter's combat effectiveness very well. As a result of simulation, the destructive rate for enemy armored corps per each attack helicopter can be calculated. In this paper, we propose optimal requirement of attack helicopter using that destructive rate for enemy armored corps.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Publications of The Korean Astronomical Society
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• v.22 no.4
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• pp.169-175
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• 2007

The international cooperation project CIBER (Cosmic Infrared Background ExpeRiment) is a rocket-borne instrument, of which the scientific goal is to measure the cosmic near-infrared extra-galactic background to search for signatures of primordial galaxy formation. CIBER consists of a wide-field two-color camera, a low-resolution absolute spectrometer, and a high-resolution narrow-band imaging spectrometer. Currently, all the subsystems have been built, and the integration, testing, and calibration of the CIBER system are on process for the scheduled launch in June 2008.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.429-438
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• 2021

Owing to the development of science technology, particularly the smart concept and defense policy factors of the 4th industry, military weapon systems are advanced, and the scientific and operational force is reduced dramatically. The aspect of the future war is characterized by the operation of troops with reduced forces from advanced and scientific weapon systems in an operational area that has expanded more than four times compared to the present. Reflecting on these situational factors, it is necessary to improve combat methods based on the changes in the battlefield environment and advanced weapon systems. In this study, to find a more efficient future combat method in a changing war pattern, this study applied the battle experiment methodology using Vision21 war game model, which is an analytical model used by the army. Finally, this study aimed to verify the future combat method and unit structure. Therefore, the scenario composition and experiment method that reflect the change in the ground operational environment and weapon system was first composed. Subsequently, an analysis method based on the combat effectiveness was applied to verify the effective combat performance method and unit structure of future infantry units.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.11
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• pp.1112-1120
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• 2008

The real-time distributed simulation at the present age concentrates on the construction of a system development environment in order to accomplish a synthetic battlefield environment connected with Live-Virtual-Constructive simulation and to realize the Simulation Based Acquisition which supports the life cycle of weapon system. Accordingly this paper describes the development environment of the UML modeling and simulation which integrates the system analysis and design methods performed during the conceptual design phase of the reconnaissance UAV system development. An integrated framework linked with the UML simulation and X-plane visualization is suggested to efficiently perform the system analysis and design, and finally the implementation contents, the analysis of experiment results and concluding remarks are described.

• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.141.1-141.1
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• 2012

CIBER (Cosmic Infrared Background ExpeRiment) is a sounding-rocket borne experiment which is designed to find the evidence of the First stars (Pop.III stars) in the universe. They are expected to be formed between the recombination era at z ~ 1100 and the most distant quasar (z ~ 8). They have never been directly detected due to its faintness so far, but can be observed as a background radiation at around $1{\mu}m$ which is called the Cosmic Near-Infrared Background (CNB). The CIBER is successfully launched on July 10, 2010 at White Sands Missile Range, New Mexico, USA. It consists of three kinds of instruments. One of them is a LRS (Low Resolution Spectrometer) which is a refractive telescope of 5.5 cm aperture with spectral resolution of 20 ~ 30 and wavelength coverage of 0.7 to $2.0{\mu}m$ to measure the spectrum of the CNB. Since LRS detects not only CNB but also stellar components, we can study their spectral features with the broad band advantage especially at around $1{\mu}m$ which is difficult at ground observations because of the atmospheric absorption by water vapor. I identified around 300 stars from observed six fields. If we can classify their spectral types with SED fitting, we can study their physical conditions of the stellar atmosphere as well as making a stellar catalogue of continuous stellar spectrum.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.235-247
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• 2014

Based on two dimensional model partition method proposed in Part 1, Part 2 provides detailed model specification and implementation. To mathematically delineate a model's behaviors and interactions among them, we extend the DEVS (Discrete Event Systems Specification) formalism and newly propose CE-DEVS (Combat Entity-DEVS) for an upper abstraction sub-model of a combat entity model. The proposed CE-DEVS additionally define two sets and one function to reflect essential semantics for the model's behaviors explicitly. These definitions enable us to understand and represent the model's behaviors easily since they eliminate differences of meaning between real-world expressions and model specifications. For model implementation, upper abstraction sub-models are implemented with DEVSim++, while the lower sub-models are realized using the C++ language. With the use of overall modeling techniques proposed in Part 1 and 2, we can conduct constructive simulation and assess factors about combat logics as well as battle field functions of the next-generation combat entity, minimizing additional modeling efforts. From the anti-torpedo warfare experiment, we can gain interesting experimental results regarding engagement situations employing developing weapons and their tactics. Finally, we expect that this work will serve an immediate application for various engagement warfare.