• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.21-27
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• 2019

The terrestrial ATSC 3.0 broadcasting system, which is capable of converging broadcast and communication services, uses IP based technology for data transmission between broadcasting equipment. In addition, data transmission between broadcasting equipment uses IP-based technology like existing wired communication network, which has advantageous in terms of equipment construction and maintenance In case IP based data transmission technology is used, however, it may inevitably cause an error that a packet is lost during transmission depending on the network environments. In order to cope with a broadcasting accident caused by such a transmission error or a malfunction of a broadcasting apparatus, a broadcasting system is generally configured as a duplication, which can transmit a normal packet when various types of error may occur. By this reason, correction method of error packets and intelligent switching technology are essential. Therefore, in this paper, we propose a design and implementation of intelligent IP switch for Ensuring Reliability of ATSC 3.0 Broadcast Streams. The proposed intelligent IP consists of IP Stream Analysis Module, ALP Stream Analysis Module, STL Stream Analysis Module and SMPTE 2022-1 based FEC Encoding/Decoding Module.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.289-294
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• 2015

Data analysts explore collections of data to search for valuable information using various techniques and tricks. Garbage in, garbage out is a well-recognized idiom that emphasizes the importance of the quality of data in data analysis. It is therefore crucial to validate the data quality in the early stage of data analysis, and an effective method of evaluating the quality of data is hence required. In this paper, a method to visually characterize the quality of data using the notion of a saliency score is introduced. The saliency score is a measure comprising five indexes that captures certain aspects of data quality. Some experiment results are presented to show the applicability of proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.252-255
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• 1997

In this paper, we will propose the methodology of data analysis using the fuzzy property set model. In real world, the data can be represented with the object. $\theta$. and the property, $\pi$, and its has-property relation, P. Then, the conceptual space can be defined with the chosen properties. Each object has a unique location in the conceptual space. In Fuzzy mode, the fuzzy property, and fuzzy conceptual space can be redefined. To analyze data using the fuzzy property set model, the rough set need to be defined in the fuzzy conceptual space.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.618-624
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• 1999

For many soft ground embankment projects , instrumentation programs for stability and settlement management is being essential . This usually leads to generate large volume of data, which can be used for further research. Database technique is most effective method for data management . Data produced by soft ground embankment instrumentation can not be used by itself but must be reproduced using geotechnical analysis technique. In this study, a intelligent database system for softground called IDSIM was developed to examine applicability intellgent database. . The IDSIM analysis instrumentation data automatically and present results by Web/DB interface successfully.

• Journal of the Korea Safety Management & Science
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• v.17 no.3
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• pp.143-148
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• 2015

The study examined the relationship between workers' safety awareness, safety performance and the components of the intelligent image analysis system in accordance with preventing the workers from safety hazard in dangerous working area. Based on the safety performance model, we include safety knowledge, safety motivation, safety compliance and safety participation, and we also define three additional factors of the intelligent image analysis system such as functional feature, penalty and incentive by using factor analysis. SEM(Structural Equation Modeling) analyses on the data from the total of 73 workers showed that functional feature of intelligent analysis system and incentive were positively related to safety knowledge and safety motivation. And mediation effects of the relationship were verified to safety compliance and safety participation through safety knowledge as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.3-19
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• 1996

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2064-2068
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• 2010

In this paper, we introduced a sequential intelligent image analysis system(SIIAS). This system is implemented using PTZ camera with intelligent analysis algorithm and TI's Davinci DM6446. Enter, abandon, removal and cross functions are included in our system. These functions can be used individually or in combination for object monitoring and tracking. Sequential intelligent function processing is more efficient than the previous one by virtue of accurate observation, wide area monitoring and low cost.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.36-39
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• 2003

This paper proposes a simultaneous application of homogeneity analysis and fuzzy clustering with in complete data. Taking the similarity between the loss of homogeneity in homogeneity analysis and the least squares criterion in principal component analysis into account, the new objective function is defined in a similar formulation to the linear fuzzy clustering with missing values. Numerical experiment shows the characteristic properties of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.592-599
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• 2015

In big data environment, we need new approach for big data analysis, because the characteristics of big data, such as volume, variety, and velocity, can analyze entire data for inferring population. But traditional methods of statistics were focused on small data called random sample extracted from population. So, the classical analyses based on statistics are not suitable to big data analysis. To solve this problem, we propose an approach to efficient big data analysis. In this paper, we consider a big data analysis using principal component analysis, which is popular method in multivariate statistics. To verify the performance of our research, we carry out diverse simulation studies.