• Clean Technology
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• v.28 no.4
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• pp.331-338
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• 2022

The objective of this study is to prepare bead-type and pellet-type Pt (1 wt%)/Kieselguhr catalysts as hydrogenation catalysts for the polycyclic aromatic hydrocarbons (PAHs) included in pyrolysis fuel oil (PFO). The optimal reaction temperature to maximize the yield of saturated cyclic hydrocarbons during the PFO-cut hydrogenation reaction in a trickle bed reactor was determined to be 250 ℃. A hydrogen/PFO-cut flow rate ratio of 1800 was found to maximize 1-ring saturated cyclic compounds. The yield of saturated cyclic compound increased as the space velocity (LHSV) of PFO-cut decreased. The difference in hydrogenation reaction performance between the pellet catalyst and the bead catalyst was negligible. However, the catalyst impregnated by Pt after molding the Kieselguhr support (AI catalyst) showed higher hydrogenation activity than the catalyst molded after Pt impregnation on the Kieselguhr powder (BI catalyst), which was a common phenomenon in both the pellet catalysts and bead catalysts. This may be due to a higher number of active sites over the AI catalyst compared to the BI catalyst. It was confirmed that the pellet catalyst prepared by the AI method had the best reaction activity of the prepared catalysts in this study. The majority of the PFO-cut hydrogenation products were cyclic hydrocarbons ranging from C₈ to C₁₅, and C₁₁ cyclic hydrocarbons had the highest distribution. It was confirmed that both a cracking reaction and hydrogenation occurred, which shifted the carbon number distribution towards light hydrocarbons.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Convergence Security Journal
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• v.22 no.2
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• pp.131-141
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• 2022

Future battlefield domains are expanding to ground, sea, air, space, and cyber, so future military operations are expected to be carried out simultaneously and complexly in various battlefield domains. In addition, the application of convergence technologies that create innovations in all fields of economy, society, and defense, such as artificial intelligence, IoT, and big data, is being promoted. However, since the current Korean military C4I system manages warfighting function DBs in one DB server, the efficiency of combat performance is reduced utilization and distribution speed of data and operation response time. To solve this problem, research is needed on how to apply the 4th industrial revolution technologies such as AI, IoT, 5G, big data, and cloud to the Korean military C4I system, but research on this is insufficient. Therefore, this paper analyzes the problems of the current Korean military C4I system and proposes to apply the 4th industrial revolution technology in terms of operational mission, network and data link, computing environment, cyber operation, interoperability and interlocking capabilities.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.257-266
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• 2022

Inundation damage is increasing every year due to localized heavy rain and an increase of rainfall exceeding the design frequency. Accordingly, the importance of hydraulic structures for flood control and defense is also increasing. The hydraulic structures are designed according to its purpose and performance, and the amount of flood is an important calculation factor. However, in Korea, design rainfall is used as input data for hydrological analysis for the design of hydraulic structures due to the lack of sufficient data and the lack of reliability of observation data. Accurate probability rainfall and its temporal distribution are important factors to estimate the design rainfall. In practice, the regression equation of temporal distribution for the design rainfall is calculated using the cumulative rainfall percentage of Huff's quartile method. In addition, the 6^th order polynomial regression equation which shows high overall accuracy, is uniformly used. In this study, the optimized regression equation of temporal distribution is derived using the variable selection method according to the principle of parsimony in statistical modeling. The derived regression equation of temporal distribution is verified through the significance test. As a result of this study, it is most appropriate to derive the regression equation of temporal distribution using the stepwise selection method, which has the advantages of both forward selection and backward elimination.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.510-516
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• 2021

Recently, the range of utilization and demand for unmanned aerial vehicle (UAV) has been continuously increasing, and research on the construction of a separate operating system for low-altitude UAV is underway through the development of a management system separate from manned aircraft. Since low-altitude UAVs also fly in the airspace, it is essential to establish technical standards and certification systems necessary for the operation of the aircraft, and research on this is also in progress. If the operating standards and certification requirements of the aircraft are presented, a test method to confirm this should also be presented. In particular, the accuracy of small UAV's navigation required during flight is required to be more precise than that of a manned aircraft or a large UAV. It was necessary to calculate a separate navigation error. In this study, we presented a test method for deriving navigation errors that can be applied to UAVs that have difficulty in acquiring long-term operational data, which is different from existing manned aircraft, and conducted verification tests.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.597-603
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• 2023

The paradigm of military operations has evolved from platform-centric warfare to network-centric warfare and further to information-centric warfare, driven by advancements in information technology. In recent years, with the development of cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things (IoT), military operations are transitioning towards knowledge-centric warfare (KCW), based on artificial intelligence. Consequently, the military places significant emphasis on integrating advanced information and communication technologies (ICT) to establish reliable C4I (Command, Control, Communication, Computer, Intelligence) systems. This research emphasizes the need to apply data mining techniques to analyze and evaluate various aspects of C4I systems, including enhancing combat capabilities, optimizing utilization in network-based environments, efficiently distributing information flow, facilitating smooth communication, and effectively implementing knowledge sharing. Data mining serves as a fundamental technology in modern big data analysis, and this study utilizes it to analyze real-world cases and propose practical strategies to maximize the efficiency of military command and control systems. The research outcomes are expected to provide valuable insights into the performance of C4I systems and reinforce knowledge-centric warfare in contemporary military operations.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.335-345
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• 2024

In the digital era, data security has become an increasingly critical issue, drawing significant attention. Particularly, anti-tampering technology has emerged as a key defense mechanism against indiscriminate hacking and unauthorized access. This paper explores case studies that exemplify the trends in the development and application of TPM (Trusted Platform Module) and software anti-tampering technology in today's digital ecosystem. By analyzing various existing security guides and guidelines, this paper identifies ambiguous areas within them and investigates recent trends in domestic and international research on software anti-tampering. Consequently, while guidelines exist for applying anti-tampering techniques, it was found that there is a lack of methods for evaluating them. Therefore, this paper aims to propose a comprehensive and systematic evaluation framework for assessing both existing and future software anti-tampering techniques. To achieve this, it using various verification methods employed in recent research. The proposed evaluation framework synthesizes these methods, categorizing them into three aspects (functionality, implementation, performance), thereby providing a comprehensive and systematic evaluation approach for assessing software anti-tampering technology in detail.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.29-38
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• 2024

The Matched Field Processing (MFP) is an estimation method for a source range and depth based on the prediction of sound propagation. However, as the frequency increases, the prediction inaccuracy of sound propagation increases, making it difficult to estimate the source position. Recently proposed, the Frequency-Difference Matched Field Processing (FD-MFP) is known to be robust even if there is a mismatch by applying a frequency-difference autoproduct extracted from the auto-correlation of a high frequency signal. In this paper, in order to evaluate the performance of the FD-MFP using a horizontal line array, simulations were conducted in the environment of the East Sea of Korea. In the area of Bottom Bounce (BB) and Convergence Zone (CZ) where detection of a sound source is possible at a long range, and the results of localization were analyzed. According to the the FD-MFP simulations of horizontal line array, the accuracy of localization is similar or degraded compared to the conventional MFP due to diffracted field and mismatch of sound speed. There was no clear result from the simulations conforming that the FD-MFP was more robust to mismatch than the conventional MFP.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.289-295
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• 2024

This study analyzes the correlation between the accracy of rifle and the result of engagement. And estimates the improvement cost of the rifle accordingly. For this experiment, an engagement class simulation model(AWAM: Army Weapon Effectiveness Analysis Model) was used. We also selected the rifle, which is a portable weapon for the experiment. Prior to this experiment, we conducted a reliability test(VV&A: Verification, Validation and Accreditation) on the model. The VV&A process is mainly done during the development of the DM&S model, which is also necessary for the operation of the M&S. We confirmed the need for VV&A during the experiment and obtained reliable experimental results using the corrected values. In the Accuracy Experiment we found that the 20% improvement is the most effective. And we were able to estimate the cost of acquiring a rifle with a 20% higher accuracy. The cost was estimated by simple regression analysis based on the price of the current rifle. Through this study, we could know the impact of the accuracy of rifle on the experimental results and estimate the cost of improved rifle.