• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.555-558
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• 2009

In this paper, stealth technology is investigated with RCS(Radar Cross Section) reduction to minimize detection range of retroreflective echoes from enemy. Most RCS reduction comes from shaping. RAM(Radar Absorbing Materials) are applied only in areas where there are special problems. Therefore, we designed and fabricated a RAM that has absorption ability higher than 17 dB at 94 GHz for RF stealth in millimeter wave range. As a result, detection range of enemy can be reduced in the 62 percent range by using a developed RAM.

• Journal of Magnetics
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• v.17 no.2
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• pp.153-157
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• 2012

This study evaluated the ability of Diffusion-Weight Image (DWI), which is one of pulse sequences used in MRI based on the T2 weighted images, to detect samples placed within phantoms according to their size. Two identically sized phantoms, which could be inserted into the breast coil bilaterally, were prepared. Five samples with different sizes were placed in the phantoms, and the T2 weighted images and DWI were obtained. The Breast 2 channel coil of SIEMENS MAGNETOM Avanto 1.5 Tesla equipment was used for the experiments. 2D T2 weighted images were obtained using the following parameters: TR/TE = 6700/74 msec, Thickness/gap = 5/1 mm, Inversion Time (TI) = 130 ms, and matrix = $224{\times}448$. The parameters of DWI were that TR/TE = 8100/90 msec, Thickness/gap = 5/1 mm, matrix = $128{\times}128$, Inversion Time = 185 ms, and b-value = 0, 100, 300, 600, 1000 s/mm. The ratio of the sample volume on DWI compared to the T2 weighted images, which show excellent ability to detect lesions on MR images, was presented as the mean b-value. The measured b-value of the samples was obtained: 0.5${\times}$0.5 cm=0.33/0.34 square ${\times}$ cm (103%), 1${\times}$1 cm=1.28/1.25 square ${\times}$ cm (102.4%), 1.5${\times}$1.5 cm = 2.28/2.67 square ${\times}$ cm (85.39%), 2${\times}$2 cm=3.56/4.08 square ${\times}$ cm (87.25%), and 2.5${\times}$2.5 cm=7.53/8.77 square ${\times}$ cm (85.86%). In conclusion, the detection ability by the size of a sample was measured to be over 85% compared to T2 weighted image, but the detection ability of DWI was relatively lower than that of T2 weighted image.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.92-99
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• 2004

The formation degree of sustain (ITO pattern) decides quality of PDP (Plasma Display Panel). For this reason, it makes efforts in searching defects more than 30 un as 100%. Now, the existing inspection is dependent upon naked eye or microscope in off-line PDP manufacturing process. In this study developed prototype inspection system of PDP 170 glass is based on line-scan mechanism. Developed system creates information that detects and sorts kinds of defect automatically. Designed inspection technology adopts multi-vision method by slip-beam formation for the minimum of inspection time and detection algorithm is embodied in detection ability of developed system. Designed algorithm had to make good use of kernel matrix that draws up an approach to geometry. A characteristic of defects, as pin hole, substance, protrusion, are extracted from blob analysis method. Defects, as open, short, spots and et al, are distinguished by line type inspection algorithm. In experiment, we could have ensured ability of inspection that can be detected with reliability of up to 95% in about 60 seconds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4043-4060
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• 2017

Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.

• Annual Conference of KIPS
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• 2024.10a
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• pp.760-763
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• 2024

Detecting vulnerabilities in source code is essential for maintaining software security, but traditional methods like static and dynamic analysis often struggle with the complexity of modern software systems. Large Language Models (LLMs), such as GPT-4, have emerged as promising tools due to their ability to learn programming language patterns from extensive datasets. However, their application in vulnerability detection faces significant hurdles. This paper explores the key challenges limiting the effectiveness of LLMs in this domain, including limited understanding of code context, scarcity of high-quality training data, accuracy and reliability issues, constrained context windows, and lack of interpretability. We analyze how these factors impede the models' ability to detect complex vulnerabilities and discuss their implications for security-critical applications. To address these challenges, we propose several directions for improvement: developing specialized and diverse datasets, integrating LLMs with traditional static analysis tools, enhancing model architectures for better code comprehension, fostering collaboration between AI systems and human experts, and improving the interpretability of model outputs. By pursuing these strategies, we aim to enhance the capabilities of LLMs in vulnerability detection, contributing to the development of more secure and robust software systems.

• Journal of Power Electronics
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• v.14 no.6
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• pp.1272-1280
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• 2014

This paper presents solutions for fault detection and diagnosis of two-level, three phase voltage-source inverter (VSI) topologies with IGBT devices. The proposed solutions combine redundant standby VSI structures and contactors (or relays) to improve the fault-tolerant capabilities of power electronics in applications with safety requirements. The suitable combination of these elements gives the inverter the ability to maintain energy processing in the occurrence of several failure modes, including short-circuit in IGBT devices, thus extending its reliability and availability. A survey of previously developed fault-tolerant VSI structures and several aspects of failure modes, detection and isolation mechanisms within VSI is first discussed. Hardware solutions for the protection of power semiconductors with fault detection and diagnosis mechanisms are then proposed to provide conditions to isolate and replace damaged power devices (or branches) in real time. Experimental results from a prototype are included to validate the proposed solutions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1441-1447
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• 2014

A robust new algorithm for R wave detection named for Multiscale-based Peak Detection(MSPD) is assessed in this paper using MIT/BIH Arrhythmia Database. MSPD algorithm is based on a matrix composed of local maximum and find R peaks using result of standard deviation in the matrix. Furthermore, By reducing needless procedure of proposed algorithm, improve algorithm ability to detect R peak efficiently. And algorithm performance is assessed according to detection rates about various arrhythmia database.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.49-57
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• 2017

IoT can be connected through a single network not only objects which can be connected to existing internet but also objects which has communication capability. This IoT environment will be a huge change to the existing communication paradigm. However, the big security problem must be solved in order to develop further IoT. Security mechanisms reflecting these characteristics should be applied because devices participating in the IoT have low processing ability and low power. In addition, devices which perform abnormal behaviors between objects should be also detected. Therefore, in this paper, we proposed D-IDS technique for efficient detection of malicious attack nodes between devices participating in the IoT. The proposed technique performs the central detection and distribution detection to improve the performance of attack detection. The central detection monitors the entire network traffic at the boundary router using SVM technique and detects abnormal behavior. And the distribution detection combines RSSI value and reliability of node and detects Sybil attack node. The performance of attack detection against malicious nodes is improved through the attack detection process. The superiority of the proposed technique can be verified by experiments.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.54-61
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• 2018

In this study, two aspects were analyzed about the robot for removal of explosive devices. First, the cost analyses were performed to provide a reasonable solution for the acquirement of the system. It is processed by an engineering estimate method and the process was consisted of two ways : a system development expense and a mass production unit price. In additions, the resultant cost analyses were compared between the cases excluding and including a mines detection system. As results, in the case of the acquirement of the robot system for removal of explosive devices, it is recommended that the performance by improving the mines detection ability should be considered preferentially rather than the cost because the material cost for the mines detection system is negligible compared to the whole system cost. Second, as a way for improving the system performance by the mine detection function, the carbon nanotube (CNT) based sensor technology was studied in terms of sensitivity and simple productivity with presenting its preliminary experimental results. The detection electrodes were formed by a photolithography method using a photosensitive CNT paste. As results, this method was shown as a scalable and expandable technology for the excellent mines detection sensors.