• Journal of Internet Computing and Services
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• v.6 no.4
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• pp.21-35
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• 2005

Software product-lines is the software development paradigm to attain the rapid development of quality applications by customizing the reconfigurable components and composing them based on predefined software architectures. Recently various methodologies for the component-based product lines are proposed, but these don't provide the specific implementation techniques of the components in terms of variability resolution mechanism. In other hand, the several approaches to implement the component supporting the variabilities resolution are developed, but these don't define the systematic analysis and design method considering the variabilities from the initial phase. This paper proposes the integration of PLUS, the one of product line methodologies extending UML modeling, and component code generation technique in order to increase the efficiency of producing the specific product in the software product lines. In this paper, the component has the hierarchical architecture consisting of the implementation elements, and each implementation elements are implemented as XSLT scripts. The codes of the components are generated from the feature selection. Using the microwave oven product lines as case study, the development process for the reconfigurable components supporting the automatic variability resolution is described.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1074-1088
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• 2016

Successful Visual Story Telling(VST) of desire is a crucial key for the success of animation because desire is the leading power of story development of animation. An analysis of the desire of VST using the top 5 successful American feature film animations is carried out. Totally, 147 desire shots are extracted by using the proposed Objective Selection of Desire Shots(OSDS) method based on the theory of Makee's conflict and desire pursuing modeling, Maslow's 20 desire types, Greimas's actant model, and the 17 narrative process classification. In addition to them, the 5 Beat(5B) model of a scene is proposed. Five image specialists have evaluated VST of the selected 147 desire shots. For each shot, the desire type among the 20 desires and the strength are obtained. Among them, the top 3 desires(affiliation, achievement, and nurturance) appearing 51.8% are analyzed. The composition elements of shots affecting the desire type and the strength have found. These can be used for better VST of preproduction and production of animation.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.928-932
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• 2009

Most diseases are caused by complex processes of various factors. Although previous researches have tried to identify the causes of the disease, there are still lots of limitations to clarify the complex factors. Here, we present a disease classification model based on an evolutionary learning approach of combinatorial features using the data sets from the genetics and cohort studies. We implemented a system for finding the combinatorial risk factors and visualizing the results. Our results show that the proposed method not only improves classification accuracy but also identifies biologically meaningful sets of risk factors.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.761-769
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• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2379-2386
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• 2016

This study conducted an analysis using IPA metrics targeting college students to get strategic implications for marketing in the recently fast-growing mobile shopping market. The IPA analysis result about the selection of mobile shopping malls is as follows. First, out of the 21 features, 'reliability of the offered products(6.09)' had the highest level of importance and 'convenience of payment(5.29)' had the highest level of performance. Second, in the area of 'Doing great, Keep it up' 11 features were included such as 'convenience of payment' and 'reliability of the offered products'. Third, the feature that needed to be corrected in the area of 'Focus here' was 'shortening the waiting time for exchange, refund or warranty service'. Fourth, low priority areas in terms of importance and performance, there were 3 features including 'push/notification helps purchases'. Fifth, to the area of 'overdone' 4 features belonged such as 'variety in the type of products'.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.837-844
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• 2010

Prediction of the prosodic phrase boundary is one of the most important natural language processing tasks. We propose, for the natural prediction of the Korean prosodic phrase boundary, a statistical approach incorporating efficient learning features. These new features reflect the factors that affect generation of the prosodic phrase boundary better than existing learning features. Notably, moreover, such learning features, extracted according to the hand-crafted prosodic phrase boundary prediction rule, impart higher accuracy. We developed a statistical model for Korean prosodic phrase boundaries based on the proposed new features. The results were 86.63% accuracy for three levels (major break, minor break, no break) and 81.14% accuracy for six levels (major break with falling tone/rising tone, minor break with falling tone/rising tone/middle tone, no break).

• Journal of Digital Convergence
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• v.15 no.6
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• pp.391-398
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• 2017

In this paper, we compared the performance of "Network Intrusion Detection System based on attack feature selection using fuzzy control language"[1] and "Intelligent Intrusion Detection System Model for attack classification using RNN"[2]. In this paper, we compare the intrusion detection performance of two techniques using KDD CUP 99 dataset. The KDD 99 dataset contains data sets for training and test data sets that can detect existing intrusions through training. There are also data that can test whether training data and the types of intrusions that are not present in the test data can be detected. We compared two papers showing good intrusion detection performance in training and test data. In the comparative paper, there is a lack of performance to detect intrusions that exist but have no existing intrusion detection capability. Among the attack types, DoS, Probe, and R2L have high detection rate using fuzzy and U2L has a high detection rate using RNN.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.12
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• pp.982-992
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• 2017

In this paper, we introduce a method of selection for the optimal transmission polarization of a W-band seeker through the extraction of the one-dimensional scattering center of a ground tank target. We calculated the surface scattering and edge scattering using the shooting and bouncing ray tracing method of the CST A-solver. Based on 4-channel RCS data, using the one-dimensional RELAX algorithm, which is a kind of spectral estimation technique, scattering centers of ground targets were extracted. According to the changes in the polarization state and look angle, we compared and analyzed the scattering center results. Through simulation, we verified that the scattering center results can be applied when feature vectors are used for target recognition.