• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.706-715
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• 2019

This research implemented grammatical rules for relationship extraction from class diagram candidate. The problem statement is generated by our algorithm to yield class diagram and candidate relationship candidates. The relationships of class diagrams are extracted automatically from the problem statement by using Natural Language Processing (NLP). The extraction used the grammatical rules that obtained from various sources and translated into our algorithm. The performance evaluation of the extraction algorithm used ATM problem statements. The application captures the problem statement and draws automatically the relations of class diagrams using Forced-Directionality Graph algorithm. The performance evaluations show refining methods for class diagram and relationships extraction improve recall score.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6B
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• pp.450-455
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• 2008

We analyze the tag identification procedure of conventional EPC Class 1 RFID system and propose the fast anti-collision algorithm for the performance improvement of the system. In the proposed algorithm, the reader uses information of tag collisions and reduces unnecessary procedures of the conventional algorithm. We evaluate the performance of the proposed anti-collision algorithm and the conventional algorithm using mathematical analysis and simulation. According to the results, the fast anti-collision algorithm shows greatly better performance than conventional algorithm.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.386-394
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• 2019

This research will describe algorithm for class diagram extraction from problem statements. Class diagram notation consist of class name, attributes, and operations. Class diagram can be extracted from the problem statement automatically by using Natural Language Processing (NLP). The extraction results heavily depends on the algorithm and preprocessing stage. The algorithm obtained from various sources with additional rules that are obtained in the implementation phase. The evaluation features using five problem statement with different domains. The application will capture the problem statement and draw the class diagram automatically by using Windows Presentation Foundation(WPF). The classification accuracy of 100% was achieved. The final algorithm achieved 92 % of average precision score.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.18-24
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• 2008

This paper analyzed Air Interface of EPCglobal's Class-0 that is UHF band protocol among radio environment protocol standard that is used to RFID system. And embodied prescribed anticollision algorithm in protocol. Also, the improved anticollision algorithm for the Class-0 protocol is proposed and performances of anticollision algorithm are compared. Result that compare performance of standard algorithm through simulation with improved algorithm, improved Class-0 algorithm when is tag number 100, reduced 8%, and when is tag number 1000, 12.2%. According as tag number increases, total realization time of improved algorithm decreased more gradually better than prescribed algorithm. Therefore, the improved anticollision algorithm proposed in this paper is advanced method improving the performance of tag recognition in the RFID system and Ubiquitous sensor network.

• Journal of Information Technology Services
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• v.6 no.1
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• pp.151-158
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• 2007

D-class computation requires multiplication of three Boolean matrices for each of all possible triples of $n{\times}n$ Boolean matrices and search for equivalent $n{\times}n$ Boolean matrices according to a specific equivalence relation. It is easy to see that even multiplying all $n{\times}n$ Boolean matrices with themselves shows exponential time complexity and D-Class computation was left an unsolved problem due to its computational complexity. The vector-based multiplication theory shows that the multiplication of three Boolean matrices for each of all possible triples of $n{\times}n$ Boolean matrices can be done much more efficiently. However, D-Class computation requires computation of equivalent classes in addition to the efficient multiplication. The paper discusses a theory and an algorithm for efficient D-class computation, and shows execution results of the algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.820-822
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• 2010

Mobile WiMAX system provides various classes of traffic such as real-time and non-realtime services. These services have different QoS requirements and the QoS aware scheduling has been an important issue. Although many of scheduling algorithms for various services in OFDMA system have been proposed, it is needed to be modified to be applied to Mobile WiMAX system. Since Mobile WiMAX supports five kinds of service classes, it is important to take QoS characteristics of each class into consideration. In this paper, we propose an efficient packet scheduling algorithm to support QoS of each class. Proposed scheme selects a service class first considering QoS Characteristics of each class and choose an appropriate user in the selected class. Simulation results show that the proposed algorithm has better performance than the other algorithm.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.44-50
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• 2011

We propose a multi-class AdaBoost algorithm for en efficient classification of multi-class data in this paper. Traditional AdaBoost algorithm is basically a binary classifier and it has limitations when applied to multi-class data problems even though multi-class versions are available. In order to overcome the problems on the AdaBoost algorithm for multi-class classification problems, we devise an AdaBoost architecture with a training algorithm that utilizes multi-class classifiers for its weak classifiers instead of series of binary classifiers. Experiments on a image classification problem using collected Caltech Image Database are preformed. The results show that the proposed AdaBoost architecture can reduce its training time while maintaining its classification accuracy competitive when compared to Adaboost.M2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2523-2538
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• 2017

In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.