• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1067-1076
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• 2020

In this paper, an unsupervised saliency guided change detection method using UAV and aerial imagery is proposed. Regions that are more different from other areas are salient, which make them more distinct. The existence of the substantial difference between two images makes saliency proper for guiding the change detection process. Change Vector Analysis (CVA), which has the capability of extracting of overall magnitude and direction of change from multi-spectral and temporal remote sensing data, is used for generating an initial difference image. Combined with an unsupervised CVA and the saliency, Principal Component Analysis(PCA), which is possible to implemented as the guide for change detection method, is proposed for UAV and aerial images. By implementing the saliency generation on the difference map extracted via the CVA, potentially changed areas obtained, and by thresholding the saliency map, most of the interest areas correctly extracted. Finally, the PCA method is implemented to extract features, and K-means clustering is applied to detect changed and unchanged map on the extracted areas. This proposed method is applied to the image sets over the flooded and typhoon-damaged area and is resulted in 95 percent better than the PCA approach compared with manually extracted ground truth for all the data sets. Finally, we compared our approach with the PCA K-means method to show the effectiveness of the method.

• Structural Engineering and Mechanics
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• v.30 no.6
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• pp.699-712
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• 2008

The effectiveness of wavelet transform in detecting delamination damages in multilayered composite beams and plates is studied here. The damaged composite beams and plates are modeled in finite element software ABAQUS and the first few mode shapes are obtained. The mode shapes of the damaged structures are then wavelet transformed. It is observed that the distribution of wavelet coefficients can identify the damage location of beams and plates by showing higher values of wavelet coefficients at the position of damage. The effectiveness of the method is studied for different boundary conditions, damage location and size for single as well as multiple delaminations in composite beams and plates. It is observed that both discrete wavelet transform (DWT) and continuous wavelet transform (CWT) can detect the presence and location of the damaged region from the mode shapes of the structures. DWT may be used to approximately evaluate the size of the delamination area, whereas, CWT is efficient to detect smaller delamination areas in composites.

• Smart Structures and Systems
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• v.20 no.6
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• pp.683-696
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• 2017

In this study, the reliability of nonlinear ultrasonic modulation based fatigue crack detection is improved using a feature-level data fusion approach. When two ultrasonic inputs at two distinct frequencies are applied to a specimen with a fatigue crack, modulation components at the summation and difference of these two input frequencies appear. First, the spectral amplitudes of the modulation components and their spectral correlations are defined as individual features. Then, a 2D feature space is constructed by combining these two features, and the presence of a fatigue crack is identified in the feature space. The effectiveness of the proposed fatigue crack detection technique is experimentally validated through cyclic loading tests of aluminum plates, full-scale steel girders and a rotating shaft component. Subsequently, the improved reliability of the proposed technique is quantitatively investigated using receiver operating characteristic analysis. The uniqueness of this study lies in (1) improvement of nonlinear ultrasonic modulation based fatigue crack detection reliability using feature-level data fusion, (2) reference-free fatigue crack diagnosis without using the baseline data obtained from the intact condition of the structure, (3) application to full-scale steel girders and shaft component, and (4) quantitative investigation of the improved reliability using receiver operating characteristic analysis.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.85-92
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• 2024

In this paper presents a comparative analysis of the performance between the latest object detection model, YOLOv10, and its previous versions. YOLOv10 introduces NMS-Free training, an enhanced model architecture, and an efficiency-centric design, resulting in outstanding performance. Experimental results using the COCO dataset demonstrate that YOLOv10-N maintains high accuracy of 39.5% and low latency of 1.84ms, despite having only 2.3M parameters and 6.7G floating-point operations (FLOPs). The key performance metrics used include the number of model parameters, FLOPs, average precision (AP), and latency. The analysis confirms the effectiveness of YOLOv10 as a real-time object detection model across various applications. Future research directions include testing on diverse datasets, further model optimization, and expanding application scenarios. These efforts aim to further enhance YOLOv10's versatility and efficiency.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.732-742
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• 2001

The concept of an agent has become important in computer science and has been applied to the number of application domains such electronic commerce and information retrieval. But, no one has proposed yet in software test. The test agent system applied the concept of an agent to software test is new test tool. It consists of the User Interface Agent. the Test Case Selection & Testing Agent and the Regression Test Agent. Each of these agents, with their intelligent rules, carry out the tests autonomously by empolying the object-oriented test processes. This system has 2 advantages. Firstly since the tests are carried our autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, by actually showing the testing process being carried out autonomously by the 3 agents that form the TAS, we show that the TAS minimizes tester interference. By also carrying out the 4 different types of experiments on the RE-Rule, CTS-Rule, overall TAS experiment, and the fault-detection effectiveness experiment on the RE-Rule, we show the cut-down on the testing time and improvement in the fault detection effectivity.

• Journal of Veterinary Science
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• v.21 no.2
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• pp.14.1-14.10
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• 2020

African swine fever (ASF) is a highly contagious disease of domestic pigs and wild boars (WBs). Without a vaccine, early antibody and antigen detection and rapid diagnosis are crucial for the effective prevention of the disease and the employment of control measures. In Sardinia, where 3 different suid populations coexisted closely for a long time, the disease persists since 1978. The recent ASF eradication plan involves more stringent measures to combat free-ranging pigs and any kind of illegality in the pig industry. However, critical issues such as the low level of hunter cooperation with veterinary services and the time required for ASF detection in the WBs killed during the hunting season still remain. Considering the need to deliver true ASF negative carcasses as early as possible, this study focuses on the evaluation and validation of a duplex pen-side test that simultaneously detects antibodies and antigens specific to ASF virus, to improve molecular diagnosis under field conditions. The main goal was to establish the specificity of the two pen-side tests performed simultaneously and to determine their ability to detect the true ASF negative carcasses among the hunted WBs. Blood and organ samples of the WBs hunted during the 2018/2019 hunting seasons were obtained. A total of 160 animals were tested using the pen-side kit test; samples were collected for virological and serological analyses. A specificity of 98% was observed considering the official laboratory tests as gold standards. The new diagnostic techniques could facilitate faster and cost-effective control of the disease.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.57-64
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• 2023

Recently, the increased use of anti-ship guided missiles, a weapon system that detects and attacks targets in naval engagement, has come to pose a major threat to the survivability of ships. In order to improve the survivability of ships in response to such anti-ship guided missiles, many studies of means to counteract them have been conducted in militarily advanced countries. The integrated survivability of a ship can be largely divided into susceptibility, vulnerability, and recoverability, and is expressed as the conditional probability, if the ship is hit, of damage and recovery. However, as research on susceptibility is a major military secret of each country, access to it is very limited and there are few publicly available data. Therefore, in this study, a possibility of estimating the susceptibility of ships using an anti-air defense system corresponding to anti-ship guided missiles was reviewed. To this, scenarios during engagement, weapon systems mounted to counter threats, and maximum detection/battle range according to the operational situation of the defense weapon system were defined. In addition, the effectiveness of the anti-air defense system and susceptibility was calculated based on the performance of the weapon system, the crew's ability to operate the weapon system, and the detection probability of the detection/defense system. To evaluate the susceptibility estimation feasibility, the sensitivity of the detailed variables was reviewed, and the usefulness of the established process was confirmed through sensitivity analysis.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.986-1005
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• 2020

In this paper, we propose AnoVid, an automated video annotation tool based on deep neural networks, that automatically generates various meta data for each scene or shot in a long drama video containing rich elements. To this end, a novel meta data schema for drama video is designed. Based on this schema, the AnoVid video annotation tool has a total of six deep neural network models for object detection, place recognition, time zone recognition, person recognition, activity detection, and description generation. Using these models, the AnoVid can generate rich video annotation data. In addition, AnoVid provides not only the ability to automatically generate a JSON-type video annotation data file, but also provides various visualization facilities to check the video content analysis results. Through experiments using a real drama video, "Misaeing", we show the practical effectiveness and performance of the proposed video annotation tool, AnoVid.