• Title/Summary/Keyword: National Defense Data

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Ultra-WideBand Channel Measurement with Compressive Sampling for Indoor Localization (실내 위치추정을 위한 Compressive Sampling적용 Ultra-WideBand 채널 측정기법)

  • Kim, Sujin;Myung, Jungho;Kang, Joonhyuk;Sung, Tae-Kyung;Lee, Kwang-Eog
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.2
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    • pp.285-297
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    • 2015
  • In this paper, Ulta-WideBand (UWB) channel measurement and modeling based on compressive sampling (CS) are proposed. The sparsity of the channel impulse response (CIR) of the UWB signal in frequency domain enables the proposed channel measurement to have a low-complexity and to provide a comparable performance compared with the existing approaches especially used for the indoor geo-localization purpose. Furthermore, to improve the performance under noisy situation, the soft thresholding method is also investigated in solving the optimization problem for signal recovery of CS. Via numerical results, the proposed channel measurement and modeling are evaluated with the real measured data in terms of location estimation error, bandwidth, and compression ratio for indoor geo-localization using UWB system.

A Study on the Model for Preemptive Intrusion Response in the era of the Fourth Industrial Revolution (4차 산업혁명 시대의 선제적 위협 대응 모델 연구)

  • Hyang-Chang Choi
    • Convergence Security Journal
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    • v.22 no.2
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    • pp.27-42
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    • 2022
  • In the era of the Fourth Industrial Revolution, digital transformation to increase the effectiveness of industry is becoming more important to achieving the goal of industrial innovation. The digital new deal and smart defense are required for digital transformation and utilize artificial intelligence, big data analysis technology, and the Internet of Things. These changes can innovate the industrial fields of national defense, society, and health with new intelligent services by continuously expanding cyberspace. As a result, work productivity, efficiency, convenience, and industrial safety will be strengthened. However, the threat of cyber-attack will also continue to increase due to expansion of the new domain of digital transformation. This paper presents the risk scenarios of cyber-attack threats in the Fourth Industrial Revolution. Further, we propose a preemptive intrusion response model to bolster the complex security environment of the future, which is one of the fundamental alternatives to solving problems relating to cyber-attack. The proposed model can be used as prior research on cyber security strategy and technology development for preemptive response to cyber threats in the future society.

Electrical fire prediction model study using machine learning (기계학습을 통한 전기화재 예측모델 연구)

  • Ko, Kyeong-Seok;Hwang, Dong-Hyun;Park, Sang-June;Moon, Ga-Gyeong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.6
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    • pp.703-710
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    • 2018
  • Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

Server State-Based Weighted Load Balancing Techniques in SDN Environments (SDN 환경에서 서버 상태 기반 가중치 부하분산 기법)

  • Kyoung-Han, Lee;Tea-Wook, Kwon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.6
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    • pp.1039-1046
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    • 2022
  • After the COVID-19 pandemic, the spread of the untact culture and the Fourth Industrial Revolution, which generates various types of data, generated so much data that it was not compared to before. This led to higher data throughput, revealing little by little the limitations of the existing network system centered on vendors and hardware. Recently, SDN technology centered on users and software that can overcome these limitations is attracting attention. In addition, SDN-based load balancing techniques are expected to increase efficiency in the load balancing area of the server cluster in the data center, which generates and processes vast and diverse data. Unlike existing SDN load distribution studies, this paper proposes a load distribution technique in which a controller checks the state of a server according to the occurrence of an event rather than periodic confirmation through a monitoring technique and allocates a user's request by weighting it according to a load ratio. As a result of the desired experiment, the proposed technique showed a better equal load balancing effect than the comparison technique, so it is expected to be more effective in a server cluster in a large and packet-flowing data center.

Design and Implementation of a Data Analysis System for Diseases and Protein based on Components (컴포넌트 기반의 질병 및 단백질 데이터 분석 시스템의 설계 및 구현)

  • Park, Jun-Ho;Yeo, Myung-Ho;Lee, JiHee;Li, He;Kang, GwangGoo;Kwon, Hyun-Ho;Lee, JinJu;Lee, HyoJoon;Lim, JongTae;Jang, Yong-Jin;Bao, WeiWei;Kim, MiKyoung;Kang, TaeHo;Kim, HakYong;Yoo, JaeSoo
    • Proceedings of the Korea Contents Association Conference
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    • 2010.05a
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    • pp.420-422
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    • 2010
  • 최근 질병 분석 및 신약을 개발하기 위한 단백질에 대한 연구는 생명 공학의 큰 테마 중 하나이다. 질병 및 단백질 데이터를 분석하기 위한 연구는 대용량의 데이터 처리를 요구하기 때문에 과거 실험을 통해 접근하던 방식에서 벗어나 최근 IT 기술의 결합을 통해 다양한 실험 데이터를 공유하고, 연계함으로써 연구를 가속화하고 있다. 하지만 생명 공학 연구자에게 있어서 IT 지식을 기반을 둔 단백질 분석 도구를 다루는데 많은 어려움이 있다. 이러한 문제를 해결하고자, IT 연구자와 생명 공학 연구자의 협업을 통한 데이터 분석 도구를 개발이 폭넓게 시도되고 있지만, 연구자 간의 협업을 도울 수 있는 통합 인프라는 전무한 실정이다. 본 논문에서는 IT 연구자와 생명 공학 연구자의 협업을 위한 인프라로서 컴포넌트 기반의 질병 및 단백질 데이터 분석 시스템을 설계하고 구현한다.

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A Study on National Cyber Capability Assessment Methodology (국가 사이버 역량 평가 방법론 연구)

  • Kang, JungMin;Hwang, HyunUk;Lee, JongMoon;Yun, YoungTae;Bae, ByungChul;Jung, SoonYoung
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.22 no.5
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    • pp.1039-1055
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    • 2012
  • It is required for us to enhance the national cyber capability as the worldwide countries have been doing effort to strengthen their cyber capabilities. However, we are encountering the difficulty in estimating national cyber capability due to the absence of any cyber capability assessment methodology. This paper presents the national cyber capability assessment methodology which is used for settle up national cyber policy. We also introduce the result of five major nations(US, China, Japan, Russia, Korea)' cyber capability assessment using the proposed methodology. The methodology is developed using open data and includes three areas; base capability, attack capability and defense capability. The assessment result shows the in the order of US, China, Korea, Russia, Japan. As the analysis of that result, in order to enhance the our cyber capability, we recommend that first, cyber budget and human resources for the base capability should be more invested, second, the strategy for attack capability enhancement is strongly required and lastly, the patch ratio and security monitoring level should be upgraded.

Polarimetric Scattering of Sea Ice and Snow Using L-band Quad-polarized PALSAR Data in Kongsfjorden, Svalbard (북극 스발바드 콩스피오르덴 해역에서 L 밴드 PALSAR 데이터를 이용한 눈과 부빙에 의한 다중편파 산란특성 해석)

  • Jung, Jung-Soo;Yang, Chan-Su;Ouchi, Kazuo;Nakamura, Kuzaki
    • Ocean and Polar Research
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    • v.33 no.1
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    • pp.1-11
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    • 2011
  • This study describes measurements of fast ice recorded on May 23, 2009, in Kongsfjorden (translated as 'Kongs Fjord'), an inlet on the west coast of Spitsbergen in the Svalbard Archipelago. Seasonal fast ice is an important feature for Svalbard fjords, both in relation to their physical environment and also the local ecosystem, since it grows seaward from the coast and remains in place throughout the winter. Ice thickness, snow, ice properties, and wind speed were measured, while SAR (Synthetic Aperture Radar) data was observed simultaneously observed two times from ALOS-PALSAR (L-band). Measured ice thickness was about 25-35 cm while the thickness of ice floe broken from fast ice was measured as 10-15 cm. Average salinity was 1.9-2.0 ppt during the melting period. Polarimetric data was used to extract H/A/alpha-angle parameters of fast ice, ice floe, snow and glacier, which was classified into 18 classes based on these parameters. It was established that the area of fast ice represents surface scattering which indicates low and medium entropy surface scatters such as Bragg and random surfaces, while fast ice covered with snow belongs to a zone of low entropy surface scattering similar to snow-covered land surfaces. The results of this study will contribute to various interpretations of interrelationships between H/A/alpha parameters and the wave scattering Phenomenon of sea ice.

A Study on the Shelf-Life Prediction of the Domestic Single Base Propellants Ammunition : Based on 105mm High Explosive Propellants (국내 단기추진제 탄약의 저장수명 예측에 관한 연구 : 105미리 고폭탄 추진체를 중심으로)

  • Choi, Myoungjin;Park, Hyungju;Yang, Jaekyung;Baek, Janghyun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.37 no.3
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    • pp.36-42
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    • 2014
  • Domestic 105mm HE (High Explosive) shell is composed of three parts that are Fuze, Projectile and Propellants. Among three parts, propelling charge of propellants part consists of single base propellants. It has been known that the lifespan of single base propellants is affected by a storage period. These are because Nitrocellulose (NC) which is the main component of propelling gunpowder can be naturally decomposed to unstable substances similar with other nitric acid ester. Even though it cannot be prevented fundamentally from being disassembled, a decomposition product ($NO_2$, $NO_3$, and $HNO_3$) and tranquillizer DPA (Diphenylamine) having high reactivity are added into a propellant to restrain induction of automatic catalysis by a decomposition product. The decay rate of the tranquillizer is also affected by a production rate of the decomposition product of NC. Therefore, an accurate prediction of the Self-Life is required to ensure against risks such as explosion. Hereupon, this paper presents a new methodology to estimate the shelf-life of single base propellants using data of ASRP (Ammunition Stockpile Reliability Program) to domestic 105mm HE (propelling charge of propellants part). We selected four attributes that are inferred to have influence on distribution of the DPA amount in a propellant from the ASRP dataset through data mining processes. Then the selected attributes were used as independent variables in a regression analysis in order to estimate the shelf-life of single base propellants.

Development of newly recruited privates on-the-job Training Achievements Group Classification Model (신병 주특기교육 성취집단 예측모형 개발)

  • Kwak, Ki-Hyo;Suh, Yong-Moo
    • Journal of the military operations research society of Korea
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    • v.33 no.2
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    • pp.101-113
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    • 2007
  • The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

Robust Nonlinear Control of a 6 DOF Parallel Manipulator : Task Space Approach

  • Kim, Hag-Seong;Youngbo Shim;Cho, Young-Man;Lee, Kyo-Il
    • Journal of Mechanical Science and Technology
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    • v.16 no.8
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    • pp.1053-1063
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    • 2002
  • This paper presents a robust nonlinear controller for a f degree of freedom (DOF) parallel manipulator in the task space coordinates. The proposed control strategy requires information on orientations and translations in the task space unlike the joint space or link space control scheme. Although a 6 DOF sensor may provide such information in a straightforward manner, its cost calls for a more economical alternative. A novel indirect method based on the readily available length information engages as a potential candidate to replace a 6 DOF sensor. The indirect approach generates the necessary information by solving the forward kinematics and subsequently applying alpha-beta-gamma tracker With the 6 DOF signals available, a robust nonlinear task space control (RNTC) scheme is proposed based on the Lyapunov redesign method, whose stability is rigorously proved. The performance of the proposed RNTC with the new estimation scheme is evaluated via experiments. First, the results of the estimator are compared with the rate-gyro signals, which indicates excellent agreement. Then, the RNTC with on-line estimated 6 DOF data is shown to achieve excellent control performance to sinusoidal inputs, which is superior to those of a commonly used proportional-plus-integral-plus-derivative controller with a feedforward friction compensation under joint space coordinates and the nonlinear controller under task space coordinates.