• The KIPS Transactions:PartA
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• v.15A no.3
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• pp.155-160
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• 2008

RFID technology has been gradually expanding its application. One of the important performance issues in RFID systems is to resolve the collision among multi-tags identification on restricted area. We consider a new anti-collision scheme based on Class Identification algorithm using Depth-First scheme. We evaluate how much performance can be improved by Class identification algorithm in the cases of Query-tree more then 17% identification rate and 150% performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5087-5102
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• 2015

Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone models of the training sets in multi-class classifier, a one-class classifier is orderly used in the framework. Feature vectors including color filter array (CFA) interpolation coefficients estimation and multi-feature fusion is employed to verify the effectiveness of the classifier combination strategy. Experimental results demonstrate that for different feature sets, our method presents high accuracy of source identification both for the cell phone in the training sets and the outliers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1942-1950
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• 2013

This paper suggests a new method of detecting attacks of network traffic by visualizing original traffic data and applying multi-class SVM (support vector machine). The proposed method first generates 2D images from IP and ports of transmitters and receivers, and extracts linear patterns and high intensity values from the images, representing traffic attacks. It then obtains variance of ports of transmitters and receivers and extracts the number of clusters and entropy features using ISODATA algorithm. Finally, it determines through multi-class SVM if the traffic data contain DDoS, DoS, Internet worm, or port scans. Experimental results show that the suggested multi-class SVM-based algorithm can more effectively detect network traffic attacks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.217-224
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• 2011

This paper proposes DDPT(Dynamic Data Protection Technique) which solves the problem of private information exposure occurring in a dynamic database environment. The DDPT in this paper generates the MAG(Multi-Attribute Generalization) rules using multi-attributes generalization algorithm, and the EC(equivalence class) satisfying the k-anonymity according to the MAG rules. Whenever data is changed, it reconstructs the EC according to the MAC rules, and protects the identification exposure which is caused by the EC change. Also, it measures the information loss rates of the EC which satisfies the ${\ell}$-diversity. It keeps data accuracy by selecting the EC which is less than critical value and enhances private information protection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1B
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• pp.17-26
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• 2005

RFID(Radio Frequency IDentification) is a technology that automatically identifies objects attached with electronic tags by using radio wave. For the implementation of an RFID system, an anti-collision algorithm is required to identify several tags within the RFID reader's range. Few researches report the performance trade-off among anti-collision algorithms in terms of the communications traffic between the reader and tags, the identification speed, and so on. In this paper, we analyze both tree based memoryless algorithms and slot aloha based algorithms that comprise of almost every class of existing anti-collision algorithms. To compare the performance, we evaluated each class of anti-collision algorithms with respect to low-cost RFID system with 96-bit EPC(Electronic Product Code). The results show that the collision tracking tree algorithm outperforms current tree based and aloha based algorithms by at least 2 times to 50 times.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8A
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• pp.847-857
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• 2007

For the implementation of a RFID system, an anti-collision algorithm is required to identify multiple tags within the range of a RFID Reader. A Bit-by-Bit algorithm is defined by Auto ID Class 0. In this paper, we propose a SBBB(Stack Bit-by-Bit) algorithm. The SBBB algorithm save the collision position and makes a query using the saved data. SBBB improve the efficiency of collision resolution. We show the performance of the SBBB algorithm by simulation. The performance of the proposed algorithm is higher than that of BBB algorithm. Especially, the more each tag bit streams are the duplicate, the higher performance is.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.886-895
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• 2006

Fingerprint classification reduces the number of matches required in automated fingerprint identification systems by categorizing fingerprints into a predefined class. Support vector machines (SVMs), widely used in pattern classification, have produced a high accuracy rate when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with $na{\ddot{i}}ve$ Bayes classifiers. More specifically, it uses representative fingerprint features such as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and $na{\ddot{i}}ve$ Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for 5-class classification. Especially, it has effectively managed tie problems usually occurred in applying OVA SVMs to multi-class classification.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.33-43
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• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.62-69
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• 2009

For the fault diagnosis of power transformer, we develop a diagnosis algorithm based on support vector machine. The proposed fault diagnosis system consists of data acquisition, fault/normal diagnosis, and identification of fault. In data acquisition part, concentrated gases are extracted from transformer for data gas analysis. In fault/normal diagnosis part, KEPCO based decision rule is performed to separate normal state from fault types. The determination of fault type is executed by multi-class SVM in identification part. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.