• Smart Media Journal
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• v.12 no.10
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• pp.93-101
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• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.666-671
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• 2023

As international environmental regulations on ship emissions are gradually strengthened, interest in electric propulsion and hybrid propulsion ships is increasing, and various solutions are being developed and applied to these ships, especially stabilization of the power system and system efficiency. The direct current distribution system is being applied as a way to increase the power. In addition, verification and testing of safety and performance of marine DC distribution systems is required. As a result of establishing a DC distribution test bed, verifying the performance of the DC distribution (variable speed power generation) system, and analyzing fuel consumption, this study applied a variable speed power generation system that is applied to DC power distribution for ships, and converted the power output from the generator into a rectifier. A system was developed to convert direct current power to connect to the system and monitor and control these devices. Through tests using this DC distribution system, the maximum voltage was 751.5V and the minimum voltage was 731.4V, and the voltage fluctuation rate was 2.7%, confirming that the voltage is stably supplied within 3%, and a variable speed power generation system was installed according to load fluctuations. When applied, it was confirmed through testing that fuel consumption could be reduced by more than 20% depending on the section compared to the existing constant speed power generation system.

• Membrane Journal
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• v.34 no.2
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• pp.87-95
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• 2024

Recent studies explore a wide array of desalination and water treatment methods, encompassing membrane processes such as reverse osmosis (RO), nanofiltration (NF), and electrodialysis (ED) to advanced capacitive deionization (CDI) and its membrane variant (MCDI). Comparative analyses reveal ED's cost-effectiveness in low-salinity scenarios, while hybrid systems (NF-MCDI, RO-NF-MCDI) show improved salt removal and energy efficiency. Novel ion separation methods (NF-CDI, NF-FCDI) offer enhanced efficacy and energy savings. These studies also highlight the efficiency of these methods in treating complex wastewater specific to various industries. Environmental impact assessments emphasize the need for sustainability in system selection. Additionally, the integration of microfabricated sensors into membranes allows real-time monitoring, advancing technology development. These studies underscore the variety and promise of emerging desalination and water treatment technologies. They provide valuable insights for enhancing efficiency, minimizing energy usage, tackling industry-specific issues, and innovating to surpass conventional method limitations. The future of sustainable water treatment appears bright, with continual advancements focused on improving efficiency, minimizing environmental impact, and ensuring adaptability across diverse applications.

• Atmosphere
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• v.34 no.2
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• pp.123-138
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• 2024

Using 18 multi-model-based a Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathways (RCP) climate change scenarios, future changes in temperature and warmth index on the Korean Peninsula in the 21st century (2011~2100) were analyzed. In the analysis of the current climate (1981~2010), the ensemble averaged model results were found to reproduce the observed average values and spatial patterns of temperature and warmth index similarly well. In the future climate projections, temperature and warmth index are expected to rise in the 21st century compared to the current climate. They go further into the future and the higher carbon scenario (SSP5-8.5), the larger the increase. In the 21st century, in the low-carbon scenario (SSP1-2.6), temperature and warmth index are expected to rise by about 2.5℃ and 24.6%, respectively, compared to the present, while in the high-carbon scenario, they are expected to rise by about 6.2℃ and 63.9%, respectively. It was analyzed that reducing carbon emissions could contribute to reducing the increase in temperature and warmth index. The increase in the warmth index due to climate change can be positively analyzed to indicate that the effective heat required for plant growth on the Korean Peninsula will be stably secured. However, it is necessary to comprehensively consider negative aspects such as changes in growth conditions during the plant growth period, increase in extreme weather such as abnormally high temperatures, and decrease in plant diversity. This study can be used as basic scientific information for adapting to climate change and preparing response measures.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.19-24
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• 2024

The future military combat environment is rapidly expanding the role and importance of artificial intelligence (AI) in defense, aligning with the current trends of declining military populations and evolving dynamics. Particularly, in the civilian sector, AI development has surged into new domains based on foundation models, such as OpenAI's Chat-GPT, categorized as Super-Giant AI or Hyperscale AI. The U.S. Department of Defense has organized Task Force Lima under the Chief Digital and AI Office (CDAO) to conduct research on the application of Large Language Models (LLM) and generative AI. Advanced military nations like China and Israel are also actively researching the integration of Super-Giant AI into their military capabilities. Consequently, there is a growing need for research within our military regarding the potential applications and fields of application for Super-Giant AI in weapon systems. In this paper, we compare the characteristics and pros and cons of specialized AI and Super-Giant AI (Foundation Models) and explore new application areas for Super-Giant AI in weapon systems. Anticipating future application areas and potential challenges, this research aims to provide insights into effectively integrating Super-Giant Artificial Intelligence into defense operations. It is expected to contribute to the development of military capabilities, policy formulation, and international security strategies in the era of advanced artificial intelligence.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.531-544
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• 2024

The purpose of this study is to analyze traffic flow characteristics according to the market penetration rate (MPR) of autonomous vehicles (AV) on road sections where bus rapid transit (BRT) is actually operating. Furthermore, the maximum traffic volume was set through estimation of future traffic demand, and traffic flow characteristics were analyzed through traffic simulation for each scenario considering of a combination of BRT introduction and AV's MPR. To test statistical significance, Kruskal-Willis test and Jonckheere-Terpstra test were used to examine the impact of the market penetration rate of Autonomous vehicles on travel time and delay time etc. At the same time, the existence of the order relationship among travel time data according to the market penetration rate of autonomous vehicle was examined. As a result of the analysis, it was founded that the travel time significantly decreased as the MPR of AV increases in both intermittent flow and continuous flow environments. In particular, in the case of continuous flow, the law of increasing returns was satisfied in the effect of increasing travel speed and reducing travel time as the MPR of AV increases. The results of this study are expected to be used as a basic information for design plans for road reconstruction and space utilization after the commercialization of AV in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.173-182
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• 2024

In recent, there has been a trend toward primarily utilizing data-driven models employing artificial intelligence technologies, such as machine learning, for flood prediction. These data-driven models offer the advantage of utilizing pre-training results, significantly reducing the required simulation time. However, it remains that a considerable amount of flood data is necessary for the pre-training in data-driven models, while the available observed data for application is often insufficient. As an alternative, validated simulation results from physically-based models are being employed as pre-training data alongside observed data. In this context, we developed a flood mapping accelerator to generate flood maps for pre-training. The proposed accelerator automates the entire process of flood mapping, i.e., estimating flood discharge using HEC-1, calculating water surface levels using HEC-RAS, simulating channel overflow and generating flood maps using RAS Mapper. With the accelerator, users can easily prepare a database for pre-training of data-driven models from hundreds to tens of thousands of rainfall scenarios. It includes various convenient menus containing a Graphic User Interface(GUI), and its practical applicability has been validated across 26 test-beds.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.169-178
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• 2024

While stable mid- to large-scale underground hydrogen storage infrastructures are needed to meet the rapidly increasing demand for hydrogen energy, evaluating the safety of explosion vibrations in adjacent buildings is becoming important because of gas explosions in underground hydrogen storage facilities. In this study, a numerical analysis of vibration safety effects on nearby building structures was performed assuming a hydrogen gas explosion disaster scenario in a low-depth underground hydrogen storage facility. A parametric study using a meta-model was conducted to predict changes in ground dynamic behavior for each combination of ground properties and to analyze sensitivity to geotechnical influencing factors. Directly above the hydrogen storage facility, the unit weight of the ground had the greatest influence on the change in ground vibration due to the explosion, whereas, farther away from the facility, the sensitivity of dynamic properties was found to be high. In addition, in evaluating the vibration stability of ground building structures based on the predicted ground vibration data and blasting vibration tolerance criteria, in the case of large reinforced concrete building structures, the ground vibration safety was guaranteed with a separation distance of about 10-30 m.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.195-200
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• 2024

The purpose of this study is to propose optimized cognitive warfare strategies for the Korean Peninsula by analyzing recent war case studies. Through the analysis of the Armenia-Azerbaijan war, the Israel-Palestine conflict, the Ukraine-Russia war, and the Israel-Hamas conflict, it was found that the following aspects are crucial in conducting cognitive warfare: 1) applying methods according to objectives, 2) organizing and structuring appropriately to the objectives and means, and 3) utilizing various means from both civilian and military sectors. Based on these findings, cognitive warfare strategies optimized for the operational environment of the Korean Peninsula were suggested in terms of the three elements of military innovation. From the aspect of methodology, it is recommended to develop cognitive warfare scenarios based on legitimacy and legality, and to integrate roles according to the level of warfare. Regarding organization and structuring, the establishment of a national-level control tower and the construction of an integrated response system involving civilians, government, military, and police based on legislation are proposed. In terms of means, it is suggested to utilize various tools from the civilian, government, military, and police sectors, such as North Korean defectors, psychological warfare broadcasts against North Korea, social media, and cyber operations, for auditory, visual, and message delivery. In future battlefields characterized by hyper-connectivity and hyper-intelligence, the execution of cognitive warfare will become increasingly important. Therefore, it is necessary to continuously develop optimized cognitive warfare strategies for the Korean Peninsula through comprehensive national efforts.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.37-50
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• 2024

In indoor disaster scenarios such as fires or gas leak, prompt rescue operations are essential to minimize expected casualties. This paper proposes a simulation model that applies the concept of Manned-Unmanned Teaming (MUM-T) for effective search and rescue in indoor fires. To this end, we first identify appropriate agents to model search and rescue operations. The proposed agent-based simulation consists of a fire and map model, an occupant model, a drone model for occupants searching, a rescuer model for searching and rescuing occupants, and a control center model that assigns missions to drones and rescuers. The agent models identified according to the MUM-T concept interact with other agents during the fire response simulation. Simulation experiments were conducted for search and rescue operations in three scenarios, one consisting of only rescuers and two with mixed rescuers and drones for MUM-T. The simulation results indicated that a drone applied to MUM-T could replace an average of four rescuers. The simulation method proposed in this study is expected to improve the efficiency of search and rescue operations in indoor disaster situations and minimizing the loss of life.