• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.89-95
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• 2023

In January 2007, the Remains Excavation and Investigation Team of the Ministry of National Defense was established, and full-scale excavation of remains was promoted. Currently, the scope of the excavation is being expanded to Baekma Hill within the DMZ, where fierce battles were fought during the Korean War. Now, many remains and remains are being excavated in Baekma hill. Most are in damaged condition. Therefore, in this study, the original form of the excavated remains was restored using 3D scanning and 3D modeling. This digital restoration method can be an alternative to compensate for the disadvantages of the manual method. Currently, various digital restorations using 3D technology are active in the field of cultural heritage. Digitally restored materials can be used as basic data for digital heritage. Based on this, various contents related to excavation of remains and patriots and veterans can be developed. Furthermore, if digital human restoration is made based on the excavated remains, it will be possible to reproduce the appearance of the dead.

• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.17-27
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• 2022

A certain level of stability margin airworthiness criteria should be met to secure robustness against uncertainties between the real plant and the model in a flight control system design. The U.S. Department of Defense (DoD) specification of MIL-F-9490D and airworthiness certification standard of MIL-HDBK-516B uses gain and phase margin criteria of flight control system. However, the same stability margin criteria is applied at all development phases without considering the design maturity of each development phase of the aircraft. Ultimately, a problem arises when the aircraft operation envelope is excessively restricted. This paper proposes the relation of handling qualities and stability margin, and presents re-established stability margin criteria as a development phases and verification methods. The results of the research study are considered to contribute to the verification of the stability margin criteria more flexibly and effectively by applying the method to not only the currently manned developing aircrafts but also the unmanned vehicle to be developed in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.195-202
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• 2006

The infiltration route analysis is a military application using geospatial information technology. The result of the analysis would present vulnerable routes for potential enemy infiltration. In order to find the susceptible routes, optimal path search algorithms (Dijkstra's and $A^*$) were used to minimize the cost function, summation of detection probability. The cost function was produced by capability of TOD (Thermal Observation Device), results of viewshed analysis using DEM (Digital Elevation Model) and two related geospatial information coverages (obstacle and vegetation) extracted from VITD (Vector product Interim Terrain Data). With respect to 50m by 50m cells, the individual cost was computed and recorded, and then the optimal infiltration routes was found while minimizing summation of the costs on the routes. The proposed algorithm was experimented in Daejeon region in South Korea. The test results show that Dijkstra's and $A^*$ algorithms do not present significant differences, but A* algorithm shows a better efficiency. This application can be used for both infiltration and surveillance. Using simulation of moving TOD, the most vulnerable routes can be detected for infiltration purpose. On the other hands, it can be inversely used for selection of the best locations of TOD. This is an example of powerful geospatial solution for military application.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.318-327
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• 2009

In this paper, we propose a 3-D geometric localization method for near-field broadband source in shallow water environments. According to the waveguide invariant theory, slope of the interference pattern which is seen in a sensor spectrogram directly proportional to a range of the source. The relative ratio of the range between source and sensors was estimated by matching of two interference patterns in spectrogram. Then this ratio is applied to the Apollonius's circle which shows the locus of a source whose range ratio from two sensors is constant. Two Apollonius's circles from three sensors make the intersection point that means the horizontal range and the azimuth angle of the source. And this intersection point is constant with source depth. Therefore the source depth can be estimated using 3-D hyperboloid equation whose range difference from two sensors is constant. To evaluate a performance of the proposed localization algorithm, simulation is performed using acoustic propagation program and analysis of localization error is demonstrated. From simulation results, error estimate for range and depth is described within 50 m and 15 m respectively.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.445-454
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• 2024

To detect underwater targets using sonar, sonar performance analysis that reflects the ocean environment and sonar characteristics must be performed. Sonar performance modeling of passive and monostatic sonar can be performed relatively quickly even considering the ocean environment. However, since bistatic and multistatic sonar performance modeling require higher computational complexity and much more time than passive or monostatic sonar cases, they have been performed by simplifying or not considering the ocean environment. In thisstudy, the effects of reverberation and ocean environment in bistatic sonar performance were analyzed using the bistatic reverberation modeling in the Ulleung Basin of the East Sea. As the sonar operation depth approaches the sound channel axis, the influence of the bathymetry on sound propagation is reduced, and the reverberation limited environment is formed only at short distances. Finally, it was confirmed that similar trends appeared through comparison between the simplified and elaborately calculated sonar performance modeling results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.747-749
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• 2012

This paper is analyzing the SoC policy of major nations as the U.S, Japan, Europe, Taiwan, China and draw the suggestions for the development of semiconductor industry in Korea. SoC is the non-memory semiconductor to support and put into action the function of system. SoC is big market over the 200billion dollars and have a huge potential for new IT convergence market. Developed countries as the US, Japan, and Europe have enforced the industrial competitiveness by company investment and Taiwan supported the SoC Industry by government fund. Korea is No.1 superpower in DRAM semiconductor, but very weak in SoC Industry. We should secure the competitiveness of SoC Industry by the development of core technology, planning the growth policy, and building the cooperative model to leap the SoC power nation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.5
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• pp.700-707
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• 2017

Recently, the military need more various education and training because of the increasing necessity of various operation. But the education and training of the military has the various difficulties such as the limitations of time, space and finance etc. In order to overcome the difficulties, the military use Defense Modeling and Simulation(DM&S). Although the participants in training has the empirical knowledge from education and training based on the simulation, the empirical knowledge is not shared because of particular characteristics of military such as security and the change of official. This situation obstructs the improving effectiveness of education and training. The purpose of this research is the systematizing and analysing the empirical knowledge using text mining and network analysis to assist the sharing of empirical knowledge. For analysing texts or documents as the empirical knowledge, we select the text mining and network analysis. We expect our research will improve the effectiveness of education and training based on simulation of DM&S.