• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.12
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• pp.716-722
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• 2005

In this paper, we propose the audio watermarking algorithm based on the critical band of HAS(human auditory system) without audibly affecting the quality of the watermarked audio and implement the detecting algorithm on the BSS(broadcast synchronizing system) for testing the proposed algorithm. According to the audio quality test, the SNR(signal to noise ratio) of the watermarked audio objectively is 66dB above. In the robustness test, the proposed algorithm can detect the watermark more than $90\%$ from various compression(MP3, AAC), A/D and D/A conversions, sampling rate conversions and especially asynchronizing attacks. The BSS automatically switches the programs between the key station and the local station in broadcasting system. The result of reliability test of implemented system by using the real broadcasting audio has no false positive error during 30 days. Because of detecting once processing per 0.5 second, we can judge that the false positive error does not occur.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.861-862
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• 2008

This paper presents the results of iris region localization for imprecisely taken iris images in UBIRIS ver2. UBIRIS database consisting of 500 images of 24-bit RGB typed TIFF file. They were captured by conventional digital camera in visible light environment. Experimental result showed that FP(False Positive rate) and FN(False Negative rate) were about 2.2% and 29.1%, respectively.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1557-1564
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• 2017

We take genomic sequences of high-dimensional low sample size (HDLSS) without ordering of response categories into account. When constructing an appropriate test statistics in this model, the classical multivariate analysis of variance (MANOVA) approach might not be useful owing to very large number of parameters and very small sample size. For these reasons, we present a pseudo marginal model based upon the Gini-Simpson index estimated via Bayesian approach. In view of small sample size, we consider the permutation distribution by every possible n! (equally likely) permutation of the joined sample observations across G groups of (sizes $n_1,{\ldots}n_G$). We simulate data and apply false discovery rate (FDR) and positive false discovery rate (pFDR) with associated proposed test statistics to the data. And we also analyze real SARS data and compute FDR and pFDR. FDR and pFDR procedure along with the associated test statistics for each gene control the FDR and pFDR respectively at any level ${\alpha}$ for the set of p-values by using the exact conditional permutation theory.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.469-475
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• 2010

This paper presents a method of classifying discolored defects of metal pads using geometric texture for AFVI (Automated Final Vision Inspection) systems. In PCB manufacturing process, the metal pads on PCB can be oxidized and discolored partly due to various environmental factors. Nowadays the discolored defects are manually detected and rejected from the process. This paper proposes an efficient geometric texture feature, SUTF (Symmetry and Uniformity Texture Feature) based on the symmetric and uniform textural characteristics of the surface of circular metal pads for automating AFVI systems. In practical experiments with real samples acquired from a production line, 30 discolored images and 1232 roughness images are tested. The experimental results demonstrate that the proposed method using SUTFs provides better performance compared to Gabor feature with 0% FNR (False Negative Rate) and 1.46% FPR (False Positive Rate). The performance of the proposed method shows its applicability in the real manufacturing systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.3
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• pp.461-469
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• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.313-329
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• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Smart Media Journal
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• v.13 no.2
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.193-196
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• 2009

Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1801-1816
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• 2014

Because HTTP-related ports are allowed through firewalls, they are an obvious point for launching cyber attacks. In particular, malware uses HTTP protocols to communicate with their master servers. We call this an HTTP-based command and control (C&C) server. Most previous studies concentrated on the behavioral pattern of C&Cs. However, these approaches need a well-defined white list to reduce the false positive rate because there are many benign applications, such as automatic update checks and web refreshes, that have a periodic access pattern. In this paper, we focus on finding new discriminative features of HTTP-based C&Cs by analyzing HTTP activity sets. First, a C&C shows a few connections at a time (low density). Second, the content of a request or a response is changed frequently among consecutive C&Cs (high content variability). Based on these two features, we propose a novel C&C analysis mechanism that detects the HTTP-based C&C. The HAS-Analyzer can classify the HTTP-based C&C with an accuracy of more than 96% and a false positive rate of 1.3% without using any white list.