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Characteristic Property of Combustion and Internal Ballistics of Triple-Based Propellant according to Particle Size of RDX 根据 RDX 粒径确定三基推进剂的燃烧特性和内弹道特性
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.428
Soojung Son, Wonmin Lee, Woojin Lee, Daegeon Kim
The important factors in the design of the gun propellant are impetus, flame temperature and pressure. In this paper, we considered a nitrocellulose based propellant composition that replaced sensitive NG(Nitroglycerin) with RDX(Cyclotrimethylenetrinitramine) and DEGDN(Diethylene glycol dinitrate) which high energy and low sensitivity. Particle size and content of RDX are the two main factors that affect the burning stability of RDX-based propellants. Among them, the characteristics of the propellant according to the particle size of RDX were confirmed. The relative combustion rate(R.Q., Relative Quickness) of the propellant changed according to the RDX particle size, and internal ballistics of properties of propellant were also varied. The particle size of RDX can be confirmed as a major factor in the combustion and internal ballistics characteristics of the propellant.
火炮推进剂设计的重要因素是推动力、火焰温度和压力。在本文中,我们考虑了一种硝化纤维素基推进剂成分,用 RDX(环三亚甲基亚硝胺)和 DEGDN(二乙二醇二硝酸酯)取代了高能低敏的 NG(硝化甘油)。粒径和 RDX 含量是影响 RDX 类推进剂燃烧稳定性的两个主要因素。其中,根据 RDX 的粒径确定推进剂的特性。推进剂的相对燃烧速率(R.Q.,Relative Quickness)随 RDX 粒径的变化而变化,推进剂的内弹道特性也随之变化。可以确认 RDX 的粒度是影响推进剂燃烧和内弹道特性的主要因素。
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引用次数: 0
Semi-Supervised SAR Image Classification via Adaptive Threshold Selection 通过自适应阈值选择进行半监督合成孔径雷达图像分类
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.319
Jaejun Do, Minjung Yoo, Jaeseok Lee, Hyoi Moon, Sunok Kim
Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.
半监督学习是使用少量标记数据和大量未标记数据训练分类模型的好方法。我们将半监督学习应用于一个合成孔径雷达(SAR)图像分类模型,该模型的数据集数量有限,难以创建。为了解决之前的困难,半监督学习使用少量标注数据训练的模型来生成和学习伪标签。此外,很多论文使用单一固定阈值来创建伪标签。在本文中,我们提出了一种半监督合成孔径雷达(SAR)图像分类方法,该方法对每个类别采用不同的阈值,而不是所有类别共享一个固定的阈值,从而在使用少量标记数据集的情况下提高 SAR 分类性能。
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引用次数: 0
Progressive Test and Evaluation Strategy for Verification of KF-X AESA Radar Development 验证 KF-X AESA 雷达开发的渐进测试和评估战略
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.387
Shinyoung Cho, Yongkil Kwak, Hyunseok Oh, Hyesun Ju, Hongwoo Park
This paper describes a progressive test and evaluation strategy for verification of Korean Fighter eXperimental (KF-X) AESA(Active Electronically Scanned Array) radar development. Three progressive stages of development test and evaluation were officially performed from simulated test conditions to actual operating conditions according to standards: radar function/performance and avionics integration. KF-X AESA radar development is repeatedly verified by progressive stages consisting of five tests: Roof-lab ground test, System Integration Laboratory(SIL) ground test, Flying Test Bed(FTB) test, KF-X ground test, and KF-X flight test. As a result, the risk factor decreases as stages and tests progress. Therefore, development test and evaluation of KF-X AESA radar are successfully performed at low development risk.
本文介绍了用于验证韩国战斗机实验型(KF-X)AESA(有源电子扫描阵列)雷达开发的渐进式测试和评估战略。根据标准,从模拟测试条件到实际运行条件,正式进行了三个渐进阶段的开发测试和评估:雷达功能/性能和航空电子设备集成。KF-X AESA 雷达的开发通过由五项测试组成的渐进阶段反复验证:屋顶实验室地面测试、系统集成实验室(SIL)地面测试、飞行试验台(FTB)测试、KF-X 地面测试和 KF-X 飞行测试。因此,风险系数会随着阶段和测试的进展而降低。因此,KF-X AESA 雷达的开发测试和评估以较低的开发风险成功完成。
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引用次数: 0
Point Ahead Angle(PAA) Estimation and a Control Algorithm for Satellite-Pointing of the Ground Terminal in Satellite-to-Ground Optical Communication 卫星对地光通信中地面终端卫星指向的前角(PAA)估计和控制算法
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.329
Taehyun Yoon
Free-space optical communication technology enables the high-speed data transmission and excellent anti-jamming security. We conduct research on satellite-to-ground free-space optical communication links for high-speed transmission of large-capacity surveillance and reconnaissance data. Since the satellite continues to move along its orbit while the optical signal is transmitted between the satellite and the ground, the pointing angle of the beam from the ground terminal needs to be corrected by Point Ahead Angle(PAA) so that the transmitted light reaches the expected location of the satellite. In this paper, we present the algorithm for PAA estimation and control.
自由空间光通信技术可实现高速数据传输和卓越的抗干扰安全性。我们对用于高速传输大容量监视和侦察数据的卫星到地面自由空间光通信链路进行了研究。由于在卫星和地面之间传输光信号时,卫星会沿着轨道继续移动,因此需要对来自地面终端的光束的指向角进行前角(PAA)校正,以便传输的光能够到达卫星的预期位置。本文介绍了 PAA 估计和控制算法。
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引用次数: 0
A Study on Standard Process of Risk Management for Defense Systems Test Works 国防系统测试工程风险管理标准流程研究
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.364
Taeheum Na, Dongeun Heo, Youngmin Kim, Jooyeoun Lee
Today, risks created by uncertainty must be managed for successful project execution. From this perspective, applying a risk management process is very important for successful defense systems test works. This paper describes ‘the implentation of risk management process for test work’ carried out by DTERI’s process improvement activities. In this study, the concept of risk management process, and details of the risk management process are examined through PMBOK and ISO/IEC/IEEE 15288, CMMI. After that, we defined ‘Standard Process for Risk Management’ of defence systems test works. And, we describe ‘Risk Management Function’ of DTERI’s Project Management System(PMS) and the risk management process of DTERI. Finally, the effectiveness of the risk management standard process is verified through quantitative analysis.
如今,要成功执行项目,就必须管理不确定性带来的风险。从这个角度来看,应用风险管理流程对于成功开展国防系统测试工作非常重要。本文介绍了 DTERI 流程改进活动开展的 "测试工作风险管理流程植入"。在这项研究中,我们通过 PMBOK 和 ISO/IEC/IEEE 15288、CMMI,研究了风险管理流程的概念和细节。随后,我们定义了国防系统测试工程的 "风险管理标准流程"。我们还描述了 DTERI 项目管理系统(PMS)的 "风险管理功能 "和 DTERI 的风险管理流程。最后,通过定量分析验证了风险管理标准流程的有效性。
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引用次数: 0
A Simulator Development of Surface Warship Torpedo Defense System considering Bubble-Generating Wake Decoy 考虑到气泡产生的波浪诱饵的水面舰艇鱼雷防御系统模拟开发
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.416
Wooshik Kim, Myoungin Shin, Jisung Park, Ho Seuk Bae
The wake-homing underwater guided weapon that detects and tracks wake generated during voyage of a surface ship is impossible to avoid with the present acoustic deception torpedo defense system. Therefore, research on bubble-generating wake decoy is necessary to deceive wake-homing underwater guided weapon. Experiments in various environments are required to verify the effective operation method and performance of the wake decoy, but performance verification through underwater experiment is limited. In this paper, we develop a simulator for an torpedo defense system of surface ship, which is applied bubble-generating wake decoy, against acoustic, wake, and hybrid homing underwater guided weapon attack. The simulator includes surface ship model, acoustic decoy(static, mobile) model, bubble-generating wake decoy model, search and motion model of underwater guided weapon and so on. By integrating various models, MATLAB GUI simulator was developed. Through the simulation results for various environmental variables by this simulator, it is judged that effective operation method and performance verification of the bubble-generating wake decoy can be performed.
水下寻的制导武器能探测和跟踪水面舰艇航行时产生的尾流,而目前的声学欺骗鱼雷防御系统无法避开这种尾流。因此,有必要对产生气泡的尾流诱饵进行研究,以欺骗尾流归航水下制导武器。要验证尾流诱饵的有效操作方法和性能,需要在各种环境下进行实验,但通过水下实验验证性能的方法有限。本文开发了一种水面舰艇鱼雷防御系统的模拟器,该系统应用了气泡产生的尾流诱饵,可抵御声导、尾流和混合寻的水下制导武器的攻击。模拟器包括水面舰艇模型、声学诱饵(静态、移动)模型、气泡产生尾流诱饵模型、水下制导武器搜索和运动模型等。通过整合各种模型,开发了 MATLAB GUI 仿真器。通过该模拟器对各种环境变量的模拟结果,可以判断气泡产生尾流诱饵的有效操作方法和性能验证。
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引用次数: 0
A Study on Maritime Object Image Classification Using a Pruning-Based Lightweight Deep-Learning Model 使用基于剪枝的轻量级深度学习模型进行海洋物体图像分类的研究
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.346
Younghoon Han, Chunju Lee, Jaegoo Kang
Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.
深度学习模型需要大量计算,因此需要很高的计算能力。在计算环境有限的设备(如海岸监控设备)中很难使用。本研究基于 MobileNet,通过分析卷积层在训练过程中的权重变化,然后剪枝对模型影响较小的卷积层,构建了一种轻量级模型。性能比较结果表明,轻量级模型在降低计算负荷、参数、模型大小和数据处理速度的同时保持了性能。通过本研究,提出了一种构建轻量级深度学习模型的有效剪枝方法,以及通过轻量级模型在沿海监控设备等有限计算环境中高效利用设备资源的可能性。
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引用次数: 0
Research on Artillery Target Size Determination Method Considering Ballistic and Terrain Characteristics 考虑弹道和地形特征的炮弹目标大小确定方法研究
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.355
Juhee Kim, Kieun Sung
This study proposes a method for determining the optimal target size for an artillery range considering ballistics and environmental conditions. To this end, the size of the probable error of each type of ammunition and charge determined during shooting were considered, and the effect of the firing position and target terrain characteristics on the target size was analyzed. In conclusion, the size of the target increased as the range increased, and a larger target size was required for the DPICM than for the general high explosive. Accordingly, the optimal target size must be determined by considering various factors such as topographical characteristics, shooting position location, and shooting range safety standards.
本研究提出了一种考虑弹道和环境条件的方法,用于确定炮兵靶场的最佳目标尺寸。为此,考虑了射击过程中确定的每种弹药和装药的可能误差大小,并分析了射击位置和目标地形特征对目标大小的影响。总之,随着射程的增加,目标的尺寸也随之增大,与一般高爆炸药相比,DPICM 需要更大的目标尺寸。因此,在确定最佳目标尺寸时必须考虑各种因素,如地形特征、射击位置和靶场安全标准。
{"title":"Research on Artillery Target Size Determination Method Considering Ballistic and Terrain Characteristics","authors":"Juhee Kim, Kieun Sung","doi":"10.9766/kimst.2024.27.3.355","DOIUrl":"https://doi.org/10.9766/kimst.2024.27.3.355","url":null,"abstract":"This study proposes a method for determining the optimal target size for an artillery range considering ballistics and environmental conditions. To this end, the size of the probable error of each type of ammunition and charge determined during shooting were considered, and the effect of the firing position and target terrain characteristics on the target size was analyzed. In conclusion, the size of the target increased as the range increased, and a larger target size was required for the DPICM than for the general high explosive. Accordingly, the optimal target size must be determined by considering various factors such as topographical characteristics, shooting position location, and shooting range safety standards.","PeriodicalId":17292,"journal":{"name":"Journal of the Korea Institute of Military Science and Technology","volume":"312 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Empirical Study on Improving the Accuracy of Demand Forecasting Based on Multi-Machine Learning 基于多机学习提高需求预测准确性的实证研究
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.406
Myunghwa Kim, Yeonjun Lee, Sangwoo Park, Kunwoo Kim, Taehee Kim
As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.
随着军队装备的日益先进和昂贵,保障备件的成本也随着装备资产的增加而不断提高。其中,备件需求预测是军队重要的管理任务之一,其准确性直接关系到军事行动和成本管理。然而,由于备件需求具有间歇性和不规则性,使用传统的统计方法或单一的统计或机器学习模型往往难以做出准确的预测。在本文中,我们结合统计和机器学习算法,通过对天马公司备件需求数据的实验,提出了一种可以提高不规则备件需求模式需求预测准确性的模型。
{"title":"An Empirical Study on Improving the Accuracy of Demand Forecasting Based on Multi-Machine Learning","authors":"Myunghwa Kim, Yeonjun Lee, Sangwoo Park, Kunwoo Kim, Taehee Kim","doi":"10.9766/kimst.2024.27.3.406","DOIUrl":"https://doi.org/10.9766/kimst.2024.27.3.406","url":null,"abstract":"As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.","PeriodicalId":17292,"journal":{"name":"Journal of the Korea Institute of Military Science and Technology","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Patent Trend Analysis of Unmanned Ground Vehicles(UGV) using Topic Modeling 利用主题建模分析无人地面运载工具(UGV)的专利趋势
Pub Date : 2024-06-05 DOI: 10.9766/kimst.2024.27.3.395
Kihwan Kim, Chasoo Jun, Chiehoon Song, Jeonghwan Jeon
This study provides a thorough examination of Unmanned Ground Vehicles(UGVs), focusing on crucial technologies and trends across major global markets. It includes an in-depth patent analysis revealing the dominant positions of the United States and the European Union in this field. Additionally, it underscores substantial advancements made by China, Japan, and Korea since 2010. Using Latent Dirichlet Allocation(LDA)-based patent text mining, the study identified key technology areas in UGV development, such as advanced control systems, navigation technologies, power supply mechanisms, and sensing and communication tools. Through linear regression analysis, the study predicted the future paths of these technology areas, offering important insights into the evolving world of UGV technology. The findings can provide strategic guidance for stakeholders in the defense, commercial, and academic sectors, pointing out the future directions in UGV advancements.
本研究对无人地面运载工具(UGV)进行了深入研究,重点关注全球主要市场的关键技术和发展趋势。它包括一项深入的专利分析,揭示了美国和欧盟在该领域的主导地位。此外,报告还强调了中国、日本和韩国自 2010 年以来取得的重大进展。该研究利用基于潜在德里希勒分配(LDA)的专利文本挖掘,确定了 UGV 开发的关键技术领域,如先进的控制系统、导航技术、供电机制以及传感和通信工具。通过线性回归分析,研究预测了这些技术领域的未来发展路径,为了解不断发展的 UGV 技术世界提供了重要见解。研究结果可为国防、商业和学术领域的利益相关者提供战略指导,指明无人潜航器的未来发展方向。
{"title":"Patent Trend Analysis of Unmanned Ground Vehicles(UGV) using Topic Modeling","authors":"Kihwan Kim, Chasoo Jun, Chiehoon Song, Jeonghwan Jeon","doi":"10.9766/kimst.2024.27.3.395","DOIUrl":"https://doi.org/10.9766/kimst.2024.27.3.395","url":null,"abstract":"This study provides a thorough examination of Unmanned Ground Vehicles(UGVs), focusing on crucial technologies and trends across major global markets. It includes an in-depth patent analysis revealing the dominant positions of the United States and the European Union in this field. Additionally, it underscores substantial advancements made by China, Japan, and Korea since 2010. Using Latent Dirichlet Allocation(LDA)-based patent text mining, the study identified key technology areas in UGV development, such as advanced control systems, navigation technologies, power supply mechanisms, and sensing and communication tools. Through linear regression analysis, the study predicted the future paths of these technology areas, offering important insights into the evolving world of UGV technology. The findings can provide strategic guidance for stakeholders in the defense, commercial, and academic sectors, pointing out the future directions in UGV advancements.","PeriodicalId":17292,"journal":{"name":"Journal of the Korea Institute of Military Science and Technology","volume":"352 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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Journal of the Korea Institute of Military Science and Technology
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