• Journal of Information Technology Services
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• v.8 no.1
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• pp.47-58
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• 2009

The information protection security diagnosis institution was applied services since 2004, for the leveling up of public information protection and the establishment of the stability and reliability of information communication. And this security diagnosis was implemented actually on the 142 firms in 2005, the 160 firms in 2006 and the 205 firms in 2007. But this is recognized by the some firms as one of the unnecessary regulations. And there are some difficulties with collecting the subjective and reliable source data for establishing the information protection security diagnosis target. In this research, the enhanced model on the selection of information protection security diagnosis target firms was suggested by the interview with some expert and the analysis for the related actual data. By the model which are introduced from the statistical analysis of the related data and the summary of some expert's suggestions, information protection security diagnosis target can include the information telecommunication service providers taking 5 billion won as sales in a year, and web service providers like as shopping mall site, with the personal records of 2 million subscribers.

• Journal of the Ergonomics Society of Korea
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• v.24 no.3
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• pp.1-10
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• 2005

The primary objective of this study is to design input methods for assisting button selection tasks on a PDA screen. Familiar methods in the existing computing environments were investigated to develop aiding methods. Factors manipulated in the experiment included aiding method, button size, and users' prior experience with PDAs. A total of sixteen participants examined the usability of button selection tasks. Two types of button selection tasks were used as experimental tasks; one was selecting a target button, and the other was selecting multiple target buttons consecutively. The results showed that the aiding method and the button size had significant effects on the subjective satisfaction as well as the performance. In addition, users' prior experience with PDAs affected the performance significantly. The interaction between the aiding method and the button size was found to have significant effects on the performance. However, the interaction effect between the button size and the PDA experience was significant on the task performance time only for the multiple button selection tasks. Design considerations were proposed based on the experimental results. These can be applied to the PDA interaction design to make the PDAs more usable.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1166-1191
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• 2011

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

• Bulletin of the Korean Chemical Society
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• v.27 no.5
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• pp.657-662
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• 2006

Identification of accessible sites in targeted RNAs is a major limitation to the effectiveness of antisense oligonucleotides. A class of antisense oligodeoxynucleotides, known as the “10-23” DNA enzyme or DNAzyme, which is a small catalytic DNA, has been shown to efficiently cleave target RNA at purine-pyrimidine junctions in vitro. We have designed a strategy to identify accessible cleavage sites in the target RNA, which is hepatitis C virus nonstructural gene 3 (HCV NS3) RNA that encodes viral helicase and protease, from a pool of random DNAzyme library. A pool of DNAzymes of 58 nucleotides-length that possess randomized annealing arms, catalytic core sequence, and fixed 5'/3'-end flanking sequences was designed and screened for their ability to cleave the target RNA. The screening procedure, which includes binding of DNAzyme pool to the target RNA under inactive condition, selection and amplification of active DNAzymes, incubation of the selected DNAzymes with the target RNA, and target site identification on sequencing gels, identified 16 potential cleavage sites in the target RNA. Corresponding DNAzymes were constructed for the selected target sites and were tested for RNA-cleavage in terms of kinetics and accessibility. These selected DNAzymes were effective in cleaving the target RNA in the presence of $Mg^{2+}$. This strategy can be applicable to identify accessible sites in any target RNA for antisense oligonucleotides-based gene inactivation methods.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.

• Korean Journal of Cognitive Science
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• v.13 no.2
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• pp.25-36
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• 2002

Two experiments were performed to explore the effects of the types of property and the tasks on the pattern of property inference in a source selection task in which the source objects were to be selected. animals that were globally similar to the target animal was selected as possible sources when the target properties were anatomical. However animals that were strongly associated with the target property were selected when the target properties were about ability In a passive inference task where premises were given. the global similarity between the source objects and the target object differently affected the confidence of the conclusion depending on the types of the target property: The similarity between the source and the target affected the degree of confidence when the target properties were anatomical ones, but did not affect when the target properties were about ability. The results suggested that participants seemed to have primitive understanding of the relevance of sources to the target properties, but did not. spontaneously seek or use the relevant information.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.9
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• pp.733-739
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• 2017

This paper is a study on PRF selection method to accurately detect the target in the target tracking mode of airborne radar. The proposed methods are an 'optimization' method to select the closest to the center of the allowable zone considering the uncertainty of the target distance and velocity prediction and a 'quasi-optimization' method to improve the real time performance. In addition, the characteristics of the proposed methods are compared and analyzed through cost function and calculation time.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.5
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• pp.331-340
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• 2016

It is important to select proper ground motions for obtaining accurate results from response history analyses. The purpose of this study is to propose an accurate and efficient method that does not require excessive computation for selecting and scaling ground motions to match target response spectrum mean and variance. The proposed method is conceptually simple and straightforward, and it does not use a simulation algorithm that requires a sophisticated subroutine program. In this method, the desired number of ground motions are sequentially scaled and selected from a ground motion library. The proposed method gives the best selection results using Sum of Square Error and has the smallest value(=0.14). Also, The accuracy and consistency of the proposed method are verified by comparing the selection results of the proposed method with those of existing methods.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.