• Journal of IKEEE
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• 제23권4호
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• pp.1288-1294
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• 2019

The channel-achieving property made the polar code show to advantage as an error-correcting code. However, sufficient error-correction performance shows the asymptotic property that is achieved when the length of the code is long. Therefore, efficient architecture is needed to realize the implementation of very-large-scale integration for the case of long input data. Although the most basic fully parallel encoder is intuitive and easy to implement, it is not suitable for long polar codes because of the high hardware complexity. Complementing this, a partially parallel encoder was proposed which has an excellent result in terms of hardware area. Nevertheless, this method has not been completely generalized and has the disadvantage that different architectures appear depending on the hardware designer. In this paper, we propose a hardware design scheme that applies the proposed systematic approach which is optimized for bit-dimension permutations. By applying this solution, it is possible to design a generalized partially parallel encoder for long polar codes with the same intuitive architecture as a fully parallel encoder.

• The Transactions of the Korea Information Processing Society
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• 제7권11호
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• pp.3566-3575
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• 2000

In this paper, we propose an simple and efficient method to estunate the camera operation by using compressed information, which is extracted diracily from MPEG2 stream without complete decoding. In the method, the motion vector is converted into approximate optical flow by using the feature of predicted frame, because the motion vector in MPEG2 video stream is not regular sequene. And they are used to estimate the camera operation, which consist of pan, and zoom by Hough transform technique. The method provided better results than the least square method for video stream of basketball and socer games. The proposed method can have a reduced computational complexity because the information is directiv abtained in compressed domain. Additionally it can be a useful technology in content-based searching and analysis of video information. Also, the estimatd cameral operationis applicable in searching or tracking objects in MPEG2 video stream without decoding.

• Journal of the Korean Institute of Intelligent Systems
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• 제20권2호
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• pp.173-180
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• 2010

The research of the preserving privacy of sensitive information has been popular recently. Many researches about the techniques of generalizing records under k-anonymity rules have been done. Considering that data anonymity requires a lot of time and resources, it would be important to decide whether a table is vulnerable to privacy attacks before being opened in terms of the improvement of data utilization as well as the privacy protection. It is also important to check to which attack the table is vulnerable and which of anonymity methods should be applied in the table. This paper describe two possible privacy attacks based upon related references. Also, we suggest the technique to check whether data table is vulnerable to any attack of them and describe what kind of anonymity methods should be done in the table. The technique we suggest in this paper can also be applied for checking the safety of anonymity tables in which insert or delete operations occurred as well from privacy attacks.

• The KIPS Transactions:PartD
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• 제16D권2호
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Culinary science and hospitality research
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• 제17권3호
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• pp.65-75
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• 2011

The purpose of the study was to explore the relationship between satisfaction and future behavior intention of cafeteria users. Respondents of the research were cafeteria users in major colleges in Kyunggi province. A total of 300 questionnaires were distributed between October 1, 2010 and October 20, 2010 but only281 questionnaires were used for the analysis. Based on 14 selection attributes, factor analysis showed that the KMO value of 0.634, which was extracted by Verimax vertical rotation, and=610.084(p<0.001). In addition, the distributional explanation showed a total of 61.685%. Five factors were obtained as ingredients, food, menu, maintenance, and internal environment factors according to the feature of each variable. The Cronbach value according to confidence and validity tests was 0.703. The explanation was 16.3% and the influence on satisfaction included ingredients, maintenance, and internal environment. The effect on future behavior intention was significant at the level of p<0.001. This study has limitations in that it has generalized the findingson consumers of university cafeterias; therefore, follow-up studies must be conducted.

• The KIPS Transactions:PartB
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• 제13B권5호
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• pp.547-552
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• 2006

There have been various cohesion measurements studied considering reference relation among attributes and methods in a class. Generally, these cohesion measurement are camed out in one class. If the range of reference relation considered are extended from one class to two classes, we could find out the reference relation between two classes. Tn this paper, we proposed a neural network to determine the method location. Neural network is effective to predict output value from input data not to be included in training and generalize after training input and output pattern repeatedly. Learning vector is generated with 30-dimensional input vector and one target binary values of method location in a constraint that there are two classes which have less than or equal to 5 attributes and methods The result of the proposed neural network is about 95% in cross-validation and 88% in testing.

• The Journal of Natural Sciences
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• 제15권1호
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• pp.11-34
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• 2005

It is no exaggeration to say that the productivity of a research using computer simulations on complex molecular systems like biomolecules depends on the ability of the sampling algorithm to explore the relevant parts of configuration space. In this study, we investigate the properties on the mutiensemble sampling (MES) which is one of the solutions that surmount limitations of conventional sampling algorithms. Works for finding out practical systematic ways of using the MES efficiently to explore distantly separated regions in configuration space are performed. In this work, the more generalized form of weighting function for MES is used and 'cavity formation in water' is simulated using Monte Carlo. investigating the correlation of simulation parameters and the efficiency of the method, we propose a practical way of maximizing the power of the MES. We applied the way to 'cavity formation in water' and were able to explore the parts of configuration space relevant to cavities of radius from 0 to 5.6A in a single simulation.

• The KIPS Transactions:PartB
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• 제14B권2호
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korea Convergence Society
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• 제10권6호
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• pp.139-146
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• 2019

In this study, we examined how consumers who experience HMD virtual reality advertising differ in their ad attitudes and memory effects according to Presence (cognitive presence, emotional presence, media presence) and engagement level. Particularly, in the case of memory effect, the information that the user memorizes through the advertising is divided into "Attribute-Related Memory (ARM)" and "Global-Related Memory (GRM)" Respectively. First, the attitudes toward HMD based virtual reality ads are positive regardless of the type of presence. Second, in the virtual reality advertising, if the level of engagement is low, ARM that memorizes the information of the product attribute is increased, whereas if the level of engagement is high, the GRM which is the memory of the attitude-based evaluation information about the product is increased appear. Third, if cognitive presence and media presence are low, then ARM is more memorable than GRM.

• Journal of Convergence for Information Technology
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• 제12권4호
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• pp.278-285
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• 2022

This study presence in VR advertising into three types presence. Further, through an experiment, the study empirically verifies the kind of recalled information depending on the flow which forms users' attitudes towards the content. The experiment was conducted with a factor design between 3X2 subjects. Hyundai Motor's Ioniq VR video was conducted, and a questionnaire of 143 subjects was used for the study. The results revealed that positive attitudes were formed towards the advertisement used in the experiment when the level of emotional presence was the highest. In addition, higher flow levels established positive attitudes towards the advertisement. and Cognitive presence's effects on memory, ARM such as product-attribute information was found to increase when the flow level is high; however, GRM such as overall product evaluation was found to increase when the flow level is low.