• Title/Summary/Keyword: 국방기술

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A triple band printed monopole antenna with a bent branch strips for WiFi / 5G (와이파이 및 5G용 굽은 가지 스트립을 가진 삼중대역 인쇄형 모노폴 안테나)

  • Min-Woo Kim;Dong-Gi Shin;Oh-Rim Ryu;Young-Soon Lee
    • Journal of Advanced Navigation Technology
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    • v.25 no.6
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    • pp.536-542
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    • 2021
  • In this paper, we proposed a triple band printed monopole antenna with a bent branch strips for WiFi / 5G. An antenna structure in which bent strips for generating multiple resonance are attached in the form of branches was newly proposed based on a typical monopole strip vertically erected as a triple band antenna structure. The proposed antenna is designed on a FR-4 substrate with dielectric constant 4.3, thickness of 1.6 mm, and size of 28×40 mm2. The measured impedance bandwidth is 430 MHz (2.22~2.65 GHz) in the 2.4 GHz WLAN, 450 MHz (3.38~3.83 GHz) in the 3.5 GHz and 2390 MHz (4.95~7.34 GHz), In particular, it has been observed that antenna has a stable omnidirectional radiation patterns as well as gain of 1.537 dBi, 1.878 dBi and 2.337 dBi in the entire frequency band of interest.

Fault Detection Technique for PVDF Sensor Based on Support Vector Machine (서포트벡터머신 기반 PVDF 센서의 결함 예측 기법)

  • Seung-Wook Kim;Sang-Min Lee
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.5
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    • pp.785-796
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    • 2023
  • In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

Optimal deployment of sonobuoy for unmanned aerial vehicles using reinforcement learning considering the target movement (표적의 이동을 고려한 강화학습 기반 무인항공기의 소노부이 최적 배치)

  • Geunyoung Bae;Juhwan Kang;Jungpyo Hong
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.2
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    • pp.214-224
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    • 2024
  • Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

Improving target recognition of active sonar multi-layer processor through deep learning of a small amounts of imbalanced data (소수 불균형 데이터의 심층학습을 통한 능동소나 다층처리기의 표적 인식성 개선)

  • Young-Woo Ryu;Jeong-Goo Kim
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.2
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    • pp.225-233
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    • 2024
  • Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

A sea trial method of hull-mounted sonar using machine learning and numerical experiments (기계학습 및 수치실험을 활용한 선체고정형소나 해상 시운전 평가 방안)

  • Ho-seong Chang;Chang-hyun Youn;Hyung-in Ra;Kyung-won Lee;Dea-hwan Kim;Ki-man Kim
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.3
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    • pp.293-304
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    • 2024
  • In this paper, efficient and reliable methodologies for conducting sea trials to evaluate the performance of hull-mounted sonar systems is discussed. These systems undergo performance verification during ship construction via sea trials. However, the evaluation procedures often lack detailed consideration of variabilities in detection performance due to seabed topography, seasonal factors. To resolve this issue, temperature and salinity structure data were collected from 1967 to 2022 using ARGO floats and ocean observers data. The paper proposes an efficient and reliable sea trial method incorporating Bellhop modeling. Furthermore, a machine learning model applying a Physics-Informed Neural Networks was developed using the acquired data. This model predicts the sound speed profile at specific points within the sea trial area, reflecting seasonal elements of performance evaluation. In this study, we predicted the seasonal variations in sound speed structure during sea trial operations at a specific location within the trial area. We then proposed a strategy to account for the variability in detection performance caused by seasonal factors, using results from Bellhop modeling.

A study on DEMONgram frequency line extraction method using deep learning (딥러닝을 이용한 DEMON 그램 주파수선 추출 기법 연구)

  • Wonsik Shin;Hyuckjong Kwon;Hoseok Sul;Won Shin;Hyunsuk Ko;Taek-Lyul Song;Da-Sol Kim;Kang-Hoon Choi;Jee Woong Choi
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.1
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    • pp.78-88
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    • 2024
  • Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

Applications of "High Definition Digital Climate Maps" in Restructuring of Korean Agriculture (한국농업의 구조조정과 전자기후도의 역할)

  • Yun, Jin-I.
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.9 no.1
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    • pp.1-16
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    • 2007
  • The use of information on natural resources is indispensable to most agricultural activities to avoid disasters, to improve input efficiency, and to increase lam income. Most information is prepared and managed at a spatial scale called the "Hydrologic Unit" (HU), which means watershed or small river basin, because virtually every environmental problem can be handled best within a single HU. South Korea consists of 840 such watersheds and, while other watershed-specific information is routinely managed by government organizations, there are none responsible for agricultural weather and climate. A joint research team of Kyung Hee University and the Agriculture, forestry and Fisheries Information Service has begun a 4-year project funded by the Ministry of Agriculture and forestry to establish a watershed-specific agricultural weather information service based on "high definition" digital climate maps (HD-DCMs) utilizing the state of the art geospatial climatological technology. For example, a daily minimum temperature model simulating the thermodynamic nature of cold air with the aid of raster GIS and microwave temperature profiling will quantify effects of cold air drainage on local temperature. By using these techniques and 30-year (1971-2000) synoptic observations, gridded climate data including temperature, solar irradiance, and precipitation will be prepared for each watershed at a 30m spacing. Together with the climatological normals, there will be 3-hourly near-real time meterological mapping using the Korea Meteorological Administration's digital forecasting products which are prepared at a 5 km by 5 km resolution. Resulting HD-DCM database and operational technology will be transferred to local governments, and they will be responsible for routine operations and applications in their region. This paper describes the project in detail and demonstrates some of the interim results.

Eurasian Naval Power on Display: Sino-Russian Naval Exercises under Presidents Xi and Putin (유라시아 지역의 해군 전력 과시: 시진핑 주석과 푸틴 대통령 체제 하에 펼쳐지는 중러 해상합동훈련)

  • Richard Weitz
    • Maritime Security
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    • v.5 no.1
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    • pp.1-53
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    • 2022
  • One manifestation of the contemporary era of renewed great power competition has been the deepening relationship between China and Russia. Their strengthening military ties, notwithstanding their lack of a formal defense alliance, have been especially striking. Since China and Russia deploy two of the world's most powerful navies, their growing maritime cooperation has been one of the most significant international security developments of recent years. The Sino-Russian naval exercises, involving varying platforms and locations, have built on years of high-level personnel exchanges, large Russian weapons sales to China, the Sino-Russia Treaty of Friendship, and other forms of cooperation. Though the joint Sino-Russian naval drills began soon after Beijing and Moscow ended their Cold War confrontation, these exercises have become much more important during the last decade, essentially becoming a core pillar of their expanding defense partnership. China and Russia now conduct more naval exercises in more places and with more types of weapons systems than ever before. In the future, Chinese and Russian maritime drills will likely encompass new locations, capabilities, and partners-including possibly the Arctic, hypersonic delivery systems, and novel African, Asian, and Middle East partners-as well as continue such recent innovations as conducting joint naval patrols and combined arms maritime drills. China and Russia pursue several objectives through their bilateral naval cooperation. The Treaty of Good-Neighborliness and Friendly Cooperation Between the People's Republic of China and the Russian Federation lacks a mutual defense clause, but does provide for consultations about common threats. The naval exercises, which rehearse non-traditional along with traditional missions (e.g., counter-piracy and humanitarian relief as well as with high-end warfighting), provide a means to enhance their response to such mutual challenges through coordinated military activities. Though the exercises may not realize substantial interoperability gains regarding combat capabilities, the drills do highlight to foreign audiences the Sino-Russian capacity to project coordinated naval power globally. This messaging is important given the reliance of China and Russia on the world's oceans for trade and the two countries' maritime territorial disputes with other countries. The exercises can also improve their national military capabilities as well as help them learn more about the tactics, techniques, and procedures of each other. The rising Chinese Navy especially benefits from working with the Russian armed forces, which have more experience conducting maritime missions, particularly in combat operations involving multiple combat arms, than the People's Liberation Army (PLA). On the negative side, these exercises, by enhancing their combat capabilities, may make Chinese and Russian policymakers more willing to employ military force or run escalatory risks in confrontations with other states. All these impacts are amplified in Northeast Asia, where the Chinese and Russian navies conduct most of their joint exercises. Northeast Asia has become an area of intensifying maritime confrontations involving China and Russia against the United States and Japan, with South Korea situated uneasily between them. The growing ties between the Chinese and Russian navies have complicated South Korean-U.S. military planning, diverted resources from concentrating against North Korea, and worsened the regional security environment. Naval planners in the United States, South Korea, and Japan will increasingly need to consider scenarios involving both the Chinese and Russian navies. For example, South Korean and U.S. policymakers need to prepare for situations in which coordinated Chinese and Russian military aggression overtaxes the Pentagon, obligating the South Korean Navy to rapidly backfill for any U.S.-allied security gaps that arise on the Korean Peninsula. Potentially reinforcing Chinese and Russian naval support to North Korea in a maritime confrontation with South Korea and its allies would present another serious challenge. Building on the commitment of Japan and South Korea to strengthen security ties, future exercises involving Japan, South Korea, and the United States should expand to consider these potential contingencies.

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A Study on the Linkage and Development of the BRM Based National Tasks and the Policy Information Contents (BRM기반 국정과제와 정책정보콘텐츠 연계 및 구축방안에 관한 연구)

  • Younghee, Noh;Inho, Chang;Hyojung, Sim;Woojung, Kwak
    • Journal of the Korean Society for information Management
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    • v.39 no.4
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    • pp.191-213
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    • 2022
  • With a view to providing a high-quality policy information service beyond the existing national task service of the national policy information portal (POINT) of the National Library of Korea Sejong, it would be necessary to effectively provide the policy data needed for the implementation of the new national tasks. Accordingly, in this study, an attempt has been made to find a way to connect and develop the BRM-based national tasks and the policy information contents. Towards this end, first, the types of national tasks and the contents of each field and area of the government function's classification system were analyzed, with a focus placed on the 120 national tasks of the new administration. Furthermore, by comparing and analyzing the national tasks of the previous administration and the current information, the contents ought to be reflected for the development of contents related to the national tasks identified. Second, the method for linking and collecting the policy information was sought based on the analysis of the current status of policy information and the national information portal. As a result of the study, first, examining the 1st stage BRM of the national tasks, it turned out that there were 21 tasks for social welfare, 14 for unification and diplomacy, 17 for small and medium-sized businesses in industry and trade, 12 for general public administration, 8 for the economy, taxation and finance, 6 for culture, sports and tourism, science and technology, and education each, 5 for communication, public order and safety each, 4 for health, transportation and logistics, and environment each, 3 for agriculture and forestry, 2 for national defense and regional development each, and 1 for maritime and fisheries each, among others. As for the new administration, it is apparent that science technology and IT are important, and hence, it is necessary to consider such when developing the information services for the core national tasks. Second, to link the database with external organizations, it would be necessary to form a linked operation council, link and collect the information on the national tasks, and link and provide the national task-related information for the POINTs.

A Descriptive Study Of School Children's Knowledge, Attitudes And Practices Regarding Smoking (중학생들의 흡연에 대한 지식, 태도, 그리고 행동에 관한 기술적 연구)

  • Park, In-Hyae
    • Research in Community and Public Health Nursing
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    • v.7 no.2
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    • pp.420-436
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    • 1996
  • The goal of this study is to explore different risk factors for smoking and look at the relationships between knowledge, attitudes, and behavioral intentions regarding smoking among schoolchildren, in order to reduce teenage smoking. To achieve this goal a self-administered questionaire regarding smoking was provided to schoolchildren in the 7th and 8th grades in one junior high school in Jerusalem. The schoolchildren were exposed to 10-12 hours of a smoking prevention intervention program. The questionaire focused primarily on the personal characteristics, social environment, knowledge, attitudes, practices, and behavioral intentions of the children. Crosstabs were performed on each variable to determine if significant associations exist among the different variables. The statistical computer, package, SPSS PC, was used to manipulate the data along with Chi-square test. The findings were as follows : About 11% of the children aged 12-14 have ever smoked or are smoking currently, and about 24.0% of those who ever smoked started smoking at the age of 10. Boys smoke more(p<.01), poorer students(by self-perception of school performance) smoke more, and those who had peers who were smokers were more likely to smoke(p<.05). The percentage of the children who reported that either father or mother smoked was about 30%, but no statistical association was found between parental smoking and children's smoking, although trends were noted in the expected direction, i.e. more smokers among children of smokers. Only 1.1% of the children intended to smoke in the future, and 98.0% of the children indicated that they can or they might be able to withstand social pressure. Seventy percent of the children demonstrated medium to high knowledge about smoking, Males, 8th graders, better students, and those without friends who smoke had higher social pressure showed more negative attitudes(p<.01). Those with non-smoking siblings showed more negative behavioral intentions regarding smoking(p<.01), and better students showed more negative behavioral intentions. Those who had higher knowledge scores showed more negative attitudes towards smoking, but not significantly so. Those who had very negative behavioral intentions showed highly significant negative attitudes towards smoking(p<.01).

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