• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.5
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• pp.417-428
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• 2017

In this paper, we describe the architectural design, unit component hardware design and core software design(Helmet Pose Tracking Software and Terrain Elevation Data Correction Software) of IHDS(Intelligent Helmet Display System), and describe the results of unit test and integration test. According to the trend of the latest helmet display system, the specifications which includes 3D map display, FLIR(Forward Looking Infra-Red) display, hybrid helmet pose tracking, visor reflection type of binocular optical system, NVC(Night Vision Camera) display, lightweight composite helmet shell were applied to the design. Especially, we proposed unique design concepts such as the automatic correction of altitude error of 3D map data, high precision image registration, multi-color lighting optical system, transmissive image emitting surface using diffraction optical element, tracking camera minimizing latency time of helmet pose estimation and air pockets for helmet fixation on head. After completing the prototype of all system components, unit tests and system integration tests were performed to verify the functions and performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2067-2072
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• 2016

Recently, due to the development of attack techniques that can circumvent existing information protection systems, continuous threats in a form unrecognized by the user have threatened information assets. Therefore, it is necessary to support the prompt responses to anticipated attempts of APT attacks, bypass access attacks, and encryption packet attacks, which the existing systems have difficulty defending against through a single response, and to continuously monitor information protection systems with a defense strategy based on Indicators of Attack (IOA). In this paper, I suggest a centralized intelligent information protection system to support the intelligent response to a violation by discerning important assets through prevention control in a performance impact assessment about information properties in order to block the attack routes of APT; establishing information control policies through weakness/risk analyses in order to remove the risks in advance; establishing detection control by restricting interior/exterior bypass networks to server access and monitoring encrypted communications; and lastly, performing related corrective control through backup/restoration.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.488-493
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• 2015

An AddSIM(Adaptive distributed and parallel Simulation environment for Interoperable and reusable Models) is an integrated engagement simulation environment with high-resolution weapon system models for estimation and analysis of their performance and effectiveness. AddSIM can simultaneously handle the continuous dynamical system models based on continuous time, and command, control(C2) and network system models based on a discrete event. To accommodate legacies based on DEVS(Discrete Event System Specification) modeling, DEVS legacies must first be converted into AddSIM models. This paper describes how to implement DEVS models on AddSIM. In this study a method of mapping from hierarchical DEVS models to AddSIM players was developed: The hierarchical DEVS model should be flattened into a one layered model and four DEVS functions of the model, external transition, internal transition, output and time advance, should be mapped into functions of the AddSIM player.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1547-1557
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• 2013

A forced vibration model of a rail system was established using the Timoshenko beam theory to determine the dynamic response of a rail under time-varying load considering the damping effect and stiffness of the elastic foundation. By using a Fourier series and a numerical method, the critical velocity and dynamic response of the rail were obtained. The forced vibration model was verified by using FEM and Euler beam theory. The permanent deformation of the rail was predicted based on the forced vibration model. The permanent deformation and wear were observed through the experiment. Parametric studies were then conducted to investigate the effect of five design factors, i.e., rail cross-section shape, rail material density, rail material stiffness, containment stiffness, and damping coefficient between rail and containment, on four performance indices of the rail, i.e., critical velocity, maximum deflection, maximum longitudinal stress, and maximum shear stress.

• Applied Chemistry for Engineering
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• v.27 no.2
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• pp.190-194
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• 2016

Tricyclopentadiene (TCPD) is one of the important precursors for making tetrahydrotricyclopentadiene, which is well known as a next-generation fuel with high energy density. In this study, TCPD was obtained by polymerization reaction of dicyclopentadiene (DCPD) using an ionic liquid (IL) supported mesoporous silica catalysts. ILs were supported to two kinds of mesoporous silica catalysts with different pore sizes such as MCM-41 and SBA-15. Four different ILs were supported to mesoporous silicas using anionic precursors such as CuCl or $FeCl_3$ and cationic precursors such as triethylamine hydrochloride or 1-butyl-3-methylimidazolium chloride. We proved that IL supported mesoporous silicas showed better catalytic performance than those of using non-supported prestine IL in the aspect of TCPD yield and DCPD conversion. Among four kinds of IL supported mesoporous silica catalysts, CuCl-based IL supported MCM-41 system showed the highest TCPD yield.

• Journal of Advanced Navigation Technology
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• v.22 no.4
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• pp.295-301
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• 2018

Recently, GPS(Global Positioning System) array antenna technology is generally used and widely adopted as a national infrastructure structure and aero-vehicles for protection the GPS signal reception. Until now, the 2-dimensional planar array is universally used for its applications in the array antenna signal processing, however relatively higher altitude air vehicles such as UAV experiences additional null zones induced by low altitude GPS interferences which is located in a symmetry zone of antenna horizontal plane and this could make the receiving antenna pattern coverage reduction. In this paper, we improved 20% of the beam pattern receiving performance and 13 dB correlation value improvement by eliminating the interference mirror images.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.715-721
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• 2012

A threat evaluation is the technique which decides order of priority about tracks engaging with enemy by recognizing battlefield situation and making it efficient decision making. That is, in battle situation of multiple target it makes expeditious decision making and then aims at minimizing asset's damage and maximizing attack to targets. Threat value computation used in threat evaluation is calculated by sensor data which generated in battle space. Because Battle situation is unpredictable and there are various possibilities generating potential events, the damage or loss of data can make confuse decision making. Therefore, in this paper we suggest that substantial threat value calculation using learning bayesian network which makes it adapt to the varying battle situation to gain reliable results under given incomplete data and then verify this system's performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.115-123
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• 2015

The infrared light is known to be less dependent on background light compared to the visible light, and thus many applications such as remote sensing and image surveillance use the infrared image. Similar to color images, infrared images can also be degraded by hazy weather condition, and consequently the performance of the infrared image-based applications can decrease. Nevertheless, infrared image dehazing has not received significant interest. In this paper, we analyze the characteristic of infrared images, especially near-infrared (NIR) images, and present an NIR dehazing algorithm using the analyzed characteristics. In particular, a machine learning framework is adopted to obtain an accurate transmission map and several post-processing methods are used for further refinement. Experimental results show that the proposed NIR dehazing algorithm outperforms the conventional color image dehazing method for NIR image dehazing.