• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.153-160
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• 2022

Moored floating platforms have great potential in ocean engineering applications because a mooring system is necessary to keep the platform in station, which is directly related to the operational efficiency and safety of the platform. This paper briefly introduces the technical and operational details of an optical sensor for measuring the tension of mooring lines of a moored platform in waves. In order to check the performance of optical sensors, an experiment with a moored floating platform in waves is carried out in the wave tank at Changwon National University. The experiment is performed in regular waves and irregular waves with a semi-submersible and triangle platform. The performance of the optical sensor is confirmed by comparing the results of the tension of the mooring lines by the optical sensor and tension gauges. The maximum tension of the mooring lines is estimated to investigate the mooring dynamics due to the effect of the wave direction and wavelength in the regular waves. The significant value of the tension of mooring lines in various wave directions is estimated in the case of irregular waves. The results show that the optical sensor is effective in measuring the tension of the mooring lines.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.1-7
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• 2023

Convolutional neural networks have already been applied to various fields beyond human visual processing capabilities in the image processing area. However, they are exposed to a severe risk of deteriorating model performance due to the appearance of adversarial attacks. In addition, defense technology to respond to adversarial attacks is effective against the attack but is vulnerable to other types of attacks. Therefore, to respond to an adversarial attack, it is necessary to analyze how the performance of the adversarial attack deteriorates through the process inside the convolutional neural network. In this study, the adversarial attack of the Alexnet and VGG11 models was analyzed using the population sparseness index, a measure of neuronal activity in neurophysiology. Through the research, it was observed in each layer that the population sparsity index for adversarial examples showed differences from that of benign examples.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1863-1871
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• 2022

In the era of Fourth Industrial Revolution, secure networking technology is playing an essential role in the defense weapon systems. Encryption technology is used for information security. The safety of cryptographic technology, according to Kerchoff's principles, is based on secure key management of cryptographic technology, not on cryptographic algorithms. However, traditional centralized key management is one of the problematic issues in battlefield environments since the frequent movement of the forces and the time-varying quality of tactical networks. Alternatively, the system resources of each node used in the IoBT(Internet of Battlefield Things) environment are limited in size, capacity, and performance, so a lightweight key management system with less computation and complexity is needed than a conventional key management algorithm. This paper proposes a novel key escrow scheme in a lightweight manner for the IoBT environment. The safety and performance of the proposed technique are verified through numerical analysis and simulations.

• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.495-501
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• 2023

P-3C/K aircraft for maritime patrols that Republic of Korea Navy is using, is equipped with a variety of sensors and communication devices. Collected data from the aircraft is managed as tactical information by flight operators and stored. When the flight mission is completed, this information is transferred to tactical support center on the ground and played back or used for follow-up work through a analysis tool. During a flight mission, there are tens of thousands of detection objects within an hour in KADIZ (Korea air defense identification zone). In contrast, in TSD (tactical situation display), which displays a map when using the analysis tool, all detected objects are expressed as symbols. The increase in display symbols has a significant impact on the TSD image updating and consequently interferes with the smooth operation of operators. In this paper, we propose applying multiple threads and multiple layers to improve the performance of existing TSD. And the performance improvement is proven through the execution results.

• Applied Chemistry for Engineering
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• v.34 no.6
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• pp.619-625
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• 2023

In order to solve problems caused by the heat load of hypersonic aircraft, this study examined the optimization of the Si/Al ratio of the catalyst and nickel ion exchange to improve the performance of the hydrocarbon decomposition reaction (endothermic reaction). It was confirmed that the catalysts prepared through Si/Al ratio optimization and nickel ion exchange showed about 10% improvement in heat absorption performance compared to thermal cracking at 4 MPa and 550 ℃. FT-IR and NH₃-TPD analyses were found to identify factors affecting activity changes, and it was observed that the Si/Al ratio of the HZSM-5 catalyst was closely correlated with acid site development and catalytic activity. In addition, TGA and O₂-TPO analyses were conducted to observe the carbon deposition inhibition properties of the nickel-added catalyst.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.609-618
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• 2024

System reliability prediction in the development stage is increasingly crucial to reliability growth management to satisfy its target reliability, since modern system usually takes a form of complex composition and various complicated functions. In most cases of development stage, however, the information available for system reliability prediction is very limited, making it difficult to predict system reliability more precisely as in the production and operating stages. In this study, a system reliability prediction process is considered when the reliability-related information such as SIL (Safety Integrity Level) and MTTFd (Mean Time to Dangerous Failure) is available in the development stage. It is suggested that when the SIL or MTTFd of a system component is known and the field operational data of similar system is given, the reliability prediction could be performed using the scaling factor for the SIL or MTTFd value of the component based on the similar system's field operational data analysis. Predicting a system reliability is then adjusted with the conversion factor reflecting the temperature condition of the environment in which the system actually operates. Finally, the case of applying the proposed system reliability prediction process to a high performance mooring platform is dealt with.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.495-506
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• 2024

In this study, we propose the terrain following algorithm using model predictive control and nonlinear disturbance observer-based backstepping sliding mode controller for an aircraft system. Terrain following is important for military missions because it helps the aircraft avoid detection by the enemy radar. The model predictive control is used to replace the generating trajectory and guidance with the flight path angle constraint. In addition, the aircraft is affected to the parameter uncertainty and unknown disturbance such as wind near the mountainous terrain. Therefore, we suggest the nonlinear disturbance-based backstepping sliding mode control method for the aircraft that has highly nonlinearity to enhance flight path angle tracking performance. Through the numerical simulation, the proposed method showed the better tracking performance than the traditional backstepping method. Furthermore, the proposed method presented the terrain following maneuver maintaining the desired altitude.