• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• Journal of electromagnetic engineering and science
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• v.18 no.3
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• pp.206-211
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• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• The Journal of Society for e-Business Studies
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• v.12 no.4
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• pp.1-15
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• 2007

Traceability links are logical links between individual requirements and other system elements such as architecture descriptions, source code, and test cases. These are useful for requirements change impact analysis, requirements conflict analysis, and requirements consistency checking. However, establishing and maintaining traceability links places a big burden since complex systems have especially yield an enormous number of various artifacts. We propose a feature-oriented requirements tracing method to manage requirements with cost benefit analysis, including value consideration and intermediate catalysis using features. Our approach offers two contributions to the study of requirements tracing: (1)We introduce feature modeling as intermediate catalysis to generate traceability links between user requirements and implementation artifacts. (2)We provide value consideration with cost and efforts to identify traceability links based on prioritized requirements, thus assigning a granularity level to each feature. In this paper, we especially present the results of a case study which is carried out in Apartment Ubiquitous Platform to integrate and connect home services in an apartment complex in details.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• Journal of the Korea Society of Computer and Information
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• v.9 no.2
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• pp.19-26
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• 2004

This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this, retrieval measurement is proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval. ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

• KSCI Review
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• v.12 no.1
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• pp.9-19
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• 2004

This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this. retrieval measurement is Proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.26-32
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• 2017

VOD (Video-On Demand) Services are considered to be one of the most successful data broadcasting services, along with Electronic Program Guides (EPGs). In particular, VOD services provide supplementary revenue for broadcasting companies in addition to the existing subscription fees and advertisement-based revenue. Therefore, each broadcasting company has developed its own VOD service and constantly seeks to improve it. This leads to the development of new VOD services, so developers are considering ways to effectively handle the frequent development needs. In this background, we conducted underlying research to apply the feature-oriented analysis model to the development of VOD services. The feature-oriented analysis model used in this study is the Feature-Oriented Domain Analysis (FODA) one developed by SEI of Carnegie Mellon University. FODA provides a tool for specifying the feature model of a software domain, based on which the developers can determine the configuration of the software with the customers. This study developed a feature model of the VOD service domain and devised the functionalities and test cases in an integrated manner with the feature model. Additionally, we proposed a VOD service development process utilizing the feature model, function specification, and test cases.