• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.239-243
• /
• 2023

Due to recent small unmanned aircraft threats by North Korea and unsuccessful reponses, severe concerns about future national security have been being increasingly surged. Especially it is notable and unaccpetable to see that North Korea's small drones flying over capical areas over 7 hours were not efficiently detected and identified even though South Korea already recognized the threats since early 2010s. Meanwhile a US military troop operating THAAD system on Seung-Joo, South Korea successfully shot down unidentified small drone on Jan 2023. The threats caused by small unmanned aircraft is worrisome not only to South Korea, but also to international nations. This paper surveyed US DoD Strategy for countering small aircraft systems and suggest how to improve our current anti-dron systems so that small unmanned aircraft threats will be dynamically diminished or neutralized as fast as we can.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.3
• /
• pp.253-257
• /
• 2022

The Azerbaijani Armed Forces(AAF) that carried out a drone blitzkrieg for a short time defeated the Armed Forces of Armenia in 2020. As a result, the AAF proved that drones aren't supplementary means, but major ones in combat. Simultaneously, the AAF demonstrated that middle powers can shortly implement the revolution in military affairs(RMA) if the organizational culture for deep change are backed up. Especially, the AAF's RMA is full of suggestions to the Repulic of Korea Army(ROKA) because the ROKA wants to prepare the dronbot combat system for the future. After this, the ROKA is able to visualize how to perform the korean style drone blitzkrieg achieving a intact victory if its comabt system and structure are developed at the same time.

• Journal of Advanced Navigation Technology
• /
• v.27 no.5
• /
• pp.635-642
• /
• 2023

With the development of artificial intelligence technology, drones are evolving beyond simple remote control tools into intelligent drones that perform missions autonomously. The importance of drones is gradually gaining attention due to the use of drones in overseas military conflicts and the analysis of the future operational environment in Korea. AMAD is proposed for the rapid development of intelligent drones. In order to develop intelligent software based on AMAD, an integrated development environment (IDE) that supports users with functions such as debugging, performance evaluation, and monitoring is essential. In this paper, we define the concepts of the development environment required for intelligent software development and describe the results of reflecting them in the design of the IDE and AMAD's agents, SVI and MPD, which are interfaced with the IDE.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.73-81
• /
• 2023

The progress in science and technology has widened the scope of the battlefield, leading to the emergence of cyber electronic warfare that exploits electromagnetic waves and networks. Drones have become more important due to advancements in battery technology and navigation systems. Nevertheless, tackling drone threats comes with its own set of difficulties. Radar plays a vital role in detecting drones, offering long-range capabilities and independence from weather conditions. However, the battlefield presents unique challenges like dealing with high levels of signal noise and ensuring the safety of the detection assets. This paper proposes various approaches to improve the operation of drone detection radar in cyber electronic warfare, with a focus on enhancing signal processing techniques, utilizing low probability of interception (LPI) radar, and implementing optimized deployment strategies.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.1049-1055
• /
• 2023

The utilization of agricultural drones for pest control operations has been increasing due to its economic efficiency. However, variations in the effectiveness of these operations occur depending on the operator's proficiency. In this study, we applied a smart operating mode to overcome the limitations of manual flight mode and proposed a numerical model. Through comparative validation with prior research, we conducted experimental verification. As a result, we determined the spray time and calculation of spray area for each drone model. We selected a drone for pest control with a high similarity to the numerical model and verified it experimentally. Through this, we confirmed that the application of the smart operating mode is more effective in terms of calculation of spray area and operational efficiency compared to manual flight mode.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.9-15
• /
• 2023

This study investigated the effect of self-control on life satisfaction of youth participating in drone programs. Youth participating in drone program activities in the metropolitan area (Seoul, Gyeonggi) were conducted as questionnaires. The survey data were coded and statistically analyzed. The statistical significance level was ⍺= .05. The results of the study are as follows. First, simple task preferences and physical activity preferences affect mental health. Second, the pursuit of adventure affects happiness. Third, pursuit of adventure and self-centered factors affect autonomy and self-esteem. In conclusion, it can be judged that adolescents are partially satisfied with their lives through self-control.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.639-644
• /
• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.167-172
• /
• 2024

In major wars of the 21st century, drones are expanding beyond surveillance and reconnaissance to include land and air as well as sea and underwater for purposes such as precision strikes, suicide attacks, and cognitive warfare. These drones will perform multi-domain operations, and to this end, they will continue to develop by improving the level of autonomy and strengthening scalability based on the High-Low Mix concept. Recently, drones have been used as a major means in major wars around the world, and there seems to be a good chance that they will evolve into game changers in the future. North Korea has also been making significant efforts to operate reconnaissance and attack drones for a long time. North Korea has recently continued to engage in provocations using drones, and its capabilities are gradually becoming more sophisticated. In addition, with the recent emergence of new strategic Drones, wartime and peacetime threats such as North Korea's use of these to secure surveillance, reconnaissance and early warning capabilities against South Korea and new types of provocations are expected to be strengthened. Through this study, we hope to provide implications by analyzing the capabilities of North Korea's strategic Drones, predicting their operation patterns, and conducting active follow-up research on the establishment of a comprehensive strategy, such as our military's drone deployment and counter-drone system solutions.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.3
• /
• pp.309-316
• /
• 2021

In this paper, the electrical power output of the micro-combustor thermophotovoltiac(TPV) system was analyzed. The system consists of a micro-combustor, photonic crystals(PhCs), and photovoltaic cells(PV cells). The system has a micro-combustor that can achieve over 1,000 K surface temperature by consuming 2.5 g/h hydrogen fuel. Also, this system incorporates current state-of-the-art PhCs surfaces(2D Ta PhCs and Tandem Filter) to increase electrical power output. In addition, InGaAsSb PV cell, which bandgap is 0.55 eV, was applied to convert a wide range of radiative energy. The performance analysis shows that a single micro-combustor TPV system can produce 0.4 W ~ 27.7 W electrical power with the temperature change of emitter(900 K ~ 1,500 K) and PV cell(250 K ~ 400 K).

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.671-672
• /
• 2015

Due to harsh conditions of disaster areas, understanding of current feature of collapsed buildings, terrain, and other infrastructures is critical issue for disaster managers. However, because of difficulties in acquiring the geographical information of the disaster site such as large disaster site and limited capability of rescue workers, comprehensive site investigation of current location of survivors buried under the remains of the building is not an easy task for disaster managers. To overcome these circumstances of disaster site, this study makes use of an unmanned aerial vehicle, commonly known as a drone to effectively acquire current image data from the large disaster areas. The framework of 3D model reconstruction of disaster site using aerial imagery acquired by drones was also presented. The proposed methodology is expected to assist rescue workers and disaster managers in achieving a rapid and accurate identification of survivors under the collapsed building.