• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3151-3168
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• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.66-71
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• 2007

Isochromatics obtained from photoelastic experiment shows the stress distributions of full field of a structure under a load. Therefore stress distributions of the structure can be read at a glace through isochromatics. Many experimental data are obtained from isochromatics. And then, the various photoelastic experimental hybrid methods have been developed. Until now, monochromatic light has been used for photoelastic experimental hybrid method. Therefore the photoelastic experimental hybrid method used until now is called the photoelastic experimental hybrid method for black and white isochromatics. When stresses are analyed by photoelastic experimental hybrid method, many experimental data are needed. Therefore some fringe orders of isochromatics are needed for the photoelastic experimental hybrid method for white and black isochromatics. Therefore in this paper, the photoelastic experimental hybrid method for color isochromatics is developed. In this case, two fringe orders are enough for the experimental data of photoelastic experimental hybrid method for color isochromatics. Applying the method to stress concentration problems, its validity is confirmed. In the precision, the photoelastic experimental hybrid method for color isochromatics is better than the photoelastic experimental hybrid method for white and black isochromatics when fringe orders of isochromatics are few. When fringe orders are few, the photoelastic experimental hybrid method for color isochromatics can be used to analyze stress through few fringe orders of isochromatics.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.633-641
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• 2014

The exponential distibution is one of the most popular distributions in analyzing the lifetime data. In this paper, we propose multiply Type I hybrid censoring. And this paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply Type I hybrid censoring. The scale parameter is estimated by approximate maximum likelihood estimation methods using two different Taylor series expansion types ($AMLE_I$, $AMLE_{II}$). We also obtain the maximum likelihood estimator (MLE) of the scale parameter ${\sigma}$ under the proposed multiply Type I hybrid censored samples. We compare the estimators in the sense of the root mean square error (RMSE). The simulation procedure is repeated 10,000 times for the sample size n=20 and 40 and various censored schemes. The $AMLE_{II}$ is better than $AMLE_I$ in the sense of the RMSE.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.339-349
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• 2005

For recent years, data broadcast technology has been recognized as a very effective data delivery mechanism in mobile computing environment with a large number of cli;ents. Especially, a hybrid broadcast algorithm in real-time environment, which integrates one-way broadcast and on-demand broadcast, has an advantage of adapting the requests of clients to a limited up-link bandwidth and following the change of data access pattern. However, previous hybrid broadcasting algorithms has a problem in the methods to get a grip on the change of data access Pattern. It is caused by the diminution of requests for the data items which are contained in periodic broadcasting schedule because they are already broadcasted. To solve this problem, existing researches may remove data items in periodic broadcasting schedule over a few cycles multiplying cooling factor or find out the requests of data items with extracting them on purpose. Both of them are the artificial methods not considering the property of data. In this paper, we propose a real-time adaptive hybrid broadcasting based on data type(RTAHB-DT) to broadcast considering data property and analysis the performance of our aigorithm through simulation study.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.215-217
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• 2016

With the popularity of smart device, mobile applications are playing more and more important role in people's daily life, these applications stores various information which greatly facilitate the user's daily life. However due to the frequent transmission of data in the network also increases the risk of data leakage, more and more developers began to focus on how to protect user data. Current mainstream development models include Native development, Web development and Hybrid development. Hybrid development is based on JavaScript and HTML5, it has a cross platform advantages similar to Web Apps and a good user experience similar to Native Apps. In this paper according to the features of Hybrid applications, we proposed a security scheme in Hybrid development model implements message-level data encryption to protect user information. And through the performance evaluation we found that in some scenario the proposed security scheme has a better performance.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.297-306
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• 2004

Push-based broadcasting in wireless information services is a very effective technique to disseminate information to a massive number of clients when the number of data items is small. When the database is large, however, it nay be beneficial to integrate a pull-based (client-to-server) backchannel with the push-based broadcast approach, resulting in a hybrid data delivery. In this paper, we analyze the performance behavior of a predeclaration-based transaction processing, which was originally devised for a push-based data broadcast, in the hybrid data delivery through an extensive simulation. Our results show that the use of predeclaration-based transaction processing can provide significant performance improvement not only in a pure push data delivery, but also in a hybrid data delivery.

• International Journal of Concrete Structures and Materials
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• v.8 no.1
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• pp.27-41
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• 2014

The use of carbon fibers (CF) and glass fibers (GF) were combined to strengthen concrete flexural members. In this study, data of tensile tests of 94 hybrid carbon-glass FRP sheets and 47 carbon and GF rovings or sheets were thoroughly investigated in terms of tensile behavior. Based on comparisons between the rule of mixtures and test data, positive hybrid effects were identified for various (GF/CF) ratios. Unlike the rule of mixtures, the hybrid sheets with relatively low (GF/CF) ratios also produced pseudo-ductility. From the calibrated results obtained from experiments, a new analytical model for the stress-strain relationship of hybrid FRP sheets was proposed. Finally, the hybrid effects were verified by structural tests of concrete members strengthened with hybrid FRP sheets and either carbon or glass FRP sheets.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.5
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• pp.391-401
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• 2009

The hybrid approach for structural experiments decomposes a structure into independent substructures that can be tested or simulated. The results from the decomposed substructures are combined to predict the behaviors of the entires structure. The hybrid approach is especially useful for the hybrid pseudo-dynamic tests that overcome the limitations of size of a test structure present in a shaking table test. The development of a computer system for the hybrid experiment requires a data model that formally organizes the information involved in the hybrid experiments. This paper provides the data model for representing the information involved in the hybrid experiments, by modifying the classes and attributes for the hybrid experiments in the Lehigh Model that is one of the data models for structural experiments. The data model for the hybrid experiments includes the classes for the physical substructures being tested and the analytical substructures being analyzed, and the simulation coordinator managing the overall experiments. Some objects for classes are implemented as an example to show the links among the classes. The data model presented in this paper can be applied for developing a computer system that helps structural engineers and researchers store, share, and access the information for the hybrid experiments.