• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.185-207
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• 1998

This research investigates computer generated hybrid second-order model of two numerically based approaches to risk classification : discriminant analysis and neural networks. The hybrid second-order models are derived by rule induction using the ID3 and tested in the several different kinds of data. This new hybrid approach is designed to combine the high prediction accuracy and robustness of DA or NN with perspicuity of ID3. The hybrid model also eliminates the problem of contradictory inputs of ID3. After doing empirical test for the validity of hybrid model using small and medium companies' bankrupt data, hybrid model shows high perspicuity, high prediction accuracy for bankrupt, and simplicity for rules. The hybrid model also shows high performance regardless the type of data such as numeric data, non-numeric data, and combined data.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.173-189
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• 2009

Recently, video data has attracted many interest. That is the reason why efficient indexing schemes are required to support the content-based retrieval of video data. But most indexing schemes are not suitable for indexing a high-dimensional data except Hybrid Spill-Tree. In this paper, we propose an efficient high-dimensional indexing scheme to support the content-based retrieval of video data. For this, we extend Hybrid Spill-Tree by using a newly designed clustering technique and by adopting a signature method. Finally, we show that proposed signature-based high dimensional indexing scheme achieves better retrieval performance than existing M-Tree and Hybrid Spill-Tree.

• Journal of Broadcast Engineering
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• v.16 no.2
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• pp.360-371
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• 2011

T-DMB hybrid data service provides advanced data services while maintaining backward compatibility with legacy T-DMB receivers by using hybrid BIFS technology enabling distributed delivery of scene description information and object description information. This paper presents the hybrid service authoring framework implementing hybrid BIFS technology for creating contents for distributed delivery, and the results of experiments by using it. Hybrid service authoring framework is comprised of service creation system, service management system, and contents offering system. It enables the creation of combined hybrid data service and the splitting of the contents into two parts for ecah delivery network, data for broadcasting network and the data for mobile network. It also enables the managements of the contents. The feasibility of advanced data services while maintaining backward compatibility with the legacy T-DMB receiver has been proved by the contents created by using the hybrid authoring framework presented in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1784-1792
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• 2003

A hybrid method with supercompact multiwavelets is suggested as an efficient and practical method to compress CFD dataset. Supercompact multiwavelets provide various advantages such as compact support and orthogonality in CFD data compression. The compactness is a crucial condition for approximated representation of CFD data to avoid unnecessary interaction between remotely spaced data across various singularities such as shock and vortices. But the supercompact multiwavelet method has to fit the CFD grid size to a product of integer and power of two, m${\times}$2$^n$. To resolve this problem, the hybrid method with combination of 3, 2 and 1 dimensional version of wavelets is studied. With the hybrid method, any arbitrary size can be handled without any shrinkage or expansion of the original problem. The presented method allows high data compression ratio for fluid simulation data. Several numerical tests substantiate large data compression ratios for flow field simulation successfully.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.63-70
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• 2018

In this work, we analyze the relationship between the accumulated mileage of hybrid electric vehicle(HEV) and the data provided from vehicle parts. Data were collected while traveling over 70,000 Km in various paths. The data collected in seconds are aggregated for 10 minutes and characterized in terms of centrality, variability, normality, and so on. We examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval of a hybrid car. When the cumulative mileage interval is categorized into =< 30,000, <= 50,000, and >50,000, the statistical properties are classified by the mileage interval as 82.3% accuracy. This indicates that if the data of the vehicle parts is collected by operating the hybrid vehicle for 10 minutes, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect the abnormality of the vehicle part relative to the accumulated mileage. It can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.58-63
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• 2015

In this work, we present a novel fountain code-based hybrid P2P storage system that combines cloud storage with P2P storage. The proposed hybrid storage system minimizes data transmission time while guaranteeing high data retrieval and data privacy. In order to guarantee data privacy and storage efficiency, the user transmits encoded data after performing fountain code-based encoding. Also, the proposed algorithm guarantees the user's data retrieval by storing the data while considering each peer's survival probability. The simulation results show that the proposed algorithm enables fast completion of the upload transmission while satisfying the required data retrieval and supporting the privacy of user data under the system parameters.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.196-204
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• 2024

The security of data and information using encryption algorithms is becoming increasingly important in today's world of digital data transmission over unsecured wired and wireless communication channels. Hybrid encryption techniques combine both symmetric and asymmetric encryption methods and provide more security than public or private key encryption models. Currently, there are many techniques on the market that use a combination of cryptographic algorithms and claim to provide higher data security. Many hybrid algorithms have failed to satisfy customers in securing data and cannot prevent all types of security threats. To improve the security of digital data, it is essential to develop novel and resilient security systems as it is inevitable in the digital era. The proposed hybrid algorithm is a combination of the well-known RSA algorithm and a simple symmetric key (SSK) algorithm. The aim of this study is to develop a better encryption method using RSA and a newly proposed symmetric SSK algorithm. We believe that the proposed hybrid cryptographic algorithm provides more security and privacy.