• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• Annual Conference of KIPS
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• 2003.11c
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• pp.1351-1354
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• 2003

데이터 마이닝 기법 중 회귀분석 기법과 의사절정나무 분석 기법을 이용하여 국지기상예보칙을 작성하는 방안을 연구하였다. 회귀분석기법을 이용하여 예보값에 영향을 미치는 예보요소를 도출하고, 도출된 예보요소를 회귀분석 기법과 의사결정나무 분석 기법에 적용하여 예보칙을 작성하였다.

• Journal of the military operations research society of Korea
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• v.27 no.2
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• pp.134-148
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• 2001

In this paper, air-defense simulation model, called "DADSim", will be introduced. DADSim(Distributed Air Defense Simulation Model) was developed by Modeling&Simulation Lab of K.N.D.U.(Korea National Defence Univ) Weapon Systems Department. This model is an analysis-purpose model in the engagement-level. DADSim can simulate not only the global air-defense or Korean Peninsula but also the local air-defense or a battle field. DADSim uses the DTED(digital terrain elevation data) LeveII it for the representation of peninsula terrain characteristics. The weapon systems cooperated in the model are low/medium-range missile systems such as HAWK, NIKE, SAM. DADSim was designed in the way of object-oriented development method, implemented by C++ language. The simulation view is an event-sequenced object-orientation. For the convenience of input, output analysis, GUI(Graphic User Interface) of menu, window, dialog box, etc. are provided to the user, For the execution of DADSim, Silicon Graphic IRIX 6.3 or high version is required. DADSim can be used for the effectiveness analysis of­defence systems. Some illustrative examples will be shown in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.687-694
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• 2020

SDN and NFV are next-generation network technologies, and cloud, such as data centers, campuses, and large companies, has been established, or is actively applied by service-oriented communication companies. In particular, the Defense Integrated Data Center will be a prime example for military applications. In order for the Defense Integrated Data Center (DIDC) to become an intelligent center, it is accelerating the promotion of the "Smart Defense Integrated Data Center", which applied the latest information and communication technology (ICT). At the time of the establishment of DIDC, it plans to start building infrastructure such as cloud services at around 30% level, and expand D-Cloud to 75% through 'Cloud First'. In addition, the introduction of SDN/NFV will reduce the operation cost and manpower of DIDC, strengthen the ability to efficiently use information resources and cyber information protection systems, and increase flexibility and agility in using each system to improve efficiency in defense management in the future. Therefore, we will discuss the justification and expected effects of SDN/NFV introduction, focusing on DIDC.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.177-185
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• 2021

Over the past 40 years, Korea's defense industry has been deepening into a low-efficiency industrial structure as the government directly controls prices, quantities, and costs. By implementing the Defense Industry Building Act in 2021, the government is creating a healthy ecosystem for the defense industry and strengthening its global competitiveness. In this study, based on KPC's Productivity Management System (PMS), a diagnostic model of defense companies implemented since 2013, the on-site diagnosis was performed from 4 to 28 days depending on the size of the company data was collected based on the results. The causal relationship was analyzed through structural equation model path analysis for the effect of innovation capability on productivity performance. As a result, it suggests that defense materials suppliers should focus on which core processes to innovate and strengthen and improve their innovation capabilities.

• Journal of Plant Biotechnology
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• v.49 no.4
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• pp.300-306
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• 2022

The mitogen-activated protein kinase (MAPK) signaling cascade is essential for a wide range of cellular responses in plants, including defense responses, responses to abiotic stress, hormone signaling, and developmental processes. Recent investigations have shown that the stress, ethylene, and MAPK signaling pathways negatively affect the formation of nitrogen-fixing nodules by directly modulating the symbiotic signaling components. However, the molecular mechanisms underlying the defense responses mediated by MAPK signaling in the organogenesis of nitrogen-fixing nodules remain unclear. In the present study, I demonstrate that the Medicago truncatula mitogen-activated protein kinase kinase 5 (MtMKK5)-Medicago truncatula mitogen-activated protein kinase 3/6 (MtMPK3/6) signaling module, expressed specifically in the symbiotic nodules, promotes defense signaling, but not ethylene signaling pathways, thereby inhibiting nodule development in M. truncatula. U0126 treatment resulted in increased cell division in the nodule meristem zone due to the inhibition of MAPK signaling. The phosphorylated TEY motif in the activation domain of MtMPK3/6 was the target domain associated with specific interactions with MtMKK5. I have confirmed the physical interactions between M. truncatula nodule inception (MtNIN) and MtMPK3/6. In the presence of high expression levels of the defense-related genes FRK1 and WRKY29, MtMKK5a overexpression significantly enhanced the defense responses of Arabidopsis against Pseudomonas syringae pv. tomato DC3000 (Pst DC3000). Overall, my data show that the negative regulation of symbiotic nitrogen-fixing nodule organogenesis by defense signaling pathways is mediated by the MtMKK5-MtMPK3/6 module.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.344-354
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• 2008

The ITA meta-model descriptions being promoted by Korean government was developed to build information technology architecture for applications in public institutions. However, the application of this ITA meta-model is not easy because of the complexity and overlapping between classes and attributes which reside in the ITA meta-models. Additionally, the National Defense Architecture is planned for development using the MND-AF. Since the National Defense Architecture must align with the Government ITA for interoperability and consistency, it is crucial the differences in the meta-models between MND-AF and Government ITA must be resolved. This study presents the trade-off results between the meta-models of MND-AF and Government ITA. It also proposes a set of tailored meta-models for use with the National Defense architecture development. The tailored meta-models use an ERA (Element, Relationship, Attribute) data structure that decreases complexity and eliminates the overlapping between classes and attributes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4080-4097
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• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.