• Journal of Communications and Networks
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• v.11 no.6
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• pp.615-625
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• 2009

Worldwide inter-operability for microwave access (WiMAX) is a promising technology that provides high data throughput with low delays for various user types and modes of operation. While much research had been conducted on physical and MAC layers, little attention has been paid to a comprehensive and efficient security solution for WiMAX. We propose a hybrid security solution combining identity-based cryptography (IBC) and certificate based approaches. We provide detailed message exchange steps in order to achieve a complete security that addresses the various kind of threats identified in previous research. While attaining this goal, efficient fusion of both techniques resulted in a 53% bandwidth improvement compared to the standard's approach, PKMv2. Also, in this hybrid approach, we have clarified the key revocation procedures and key lifetimes. Consequently, to the best of knowledge our approach is the first work that unites the advantages of both techniques for improved security while maintaining the low overhead forWiMAX.

• ETRI Journal
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• v.35 no.4
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• pp.655-665
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• 2013

Hybrid digital multimedia broadcasting (DMB) is a next-generation mobile TV system that combines broadcasting and wireless communication networks and can provide various high-quality multimedia services. However, if a system adheres to the current standard of transmitting the DMB content in the form of MPEG2-TS through wireless networks, it results in a burden on the network due to low transmission efficiency. The reasons for the low transmission efficiency are as follows. First, due to its constant bitrate characteristic, DMB MPEG2-TS includes a considerable amount of needless information, such as NULL packets and stuffing bytes. Second, due to the inflexibility of the Real-time Transport Protocol (RTP) standard, one cannot fully utilize the maximum transmission unit of the network when converting MPEG2-TS to RTP stream for transmission. This paper proposes a new transmission scheme that resolves these problems. Experiment results show that the proposed scheme improves data bitrate transmission efficiency by 8% to 36%, compared to the standard scheme, in the streaming of various real-DMB contents.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.129-141
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• 2001

Kansei engineering or image technology is a tool to analyze relation between product design components and the impression or feeling of human for physical products. This paper attempts to construct the designer\`s aid tool for developing corporate identity(CI) symbol mark based on the hybrid Kansei engineering. It combines the forward Kansei engineering for translating consumer\`s feeling into design components of CI symbol mark and the backward Kansei engineering for evaluating consumer\`s feeling for CI symbol mark. The semantic differential(SD) evaluation experiment is carried out to find the relations between image and design. The backward Kansei engineering system is modelled by fuzzy neural network. This research is expected to contribute to the development of CI symbol mark that correspond to comsumer\`s image.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.355-363
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• 2022

This paper presents the modeling for islanded hybrid AC/DC microgrid and the verification of the proposed supervisory controller for energy management for this microgrid. The supervisory controller allows the microgrid system to operate in different power flows through the proposed control algorithm, it has several roles in the management of the energy flow between the different components of the microgrid for reliable operation. The proposed microgrid has both essential objectives such as the maximum use of renewable energies resources and the reduction of multiple conversion processes in an individual AC or DC microgrids. The microgrid system considered for this study has a solar photovoltaic (PV), a wind turbine (WT), a battery (BT), and a AC/DC loads. A small islanded hybrid AC/DC microgrid has been modeled and simulated using the MATLAB-Simulink. The simulation results show that the system can maintain stable operation under the proposed supervisory controller when the microgrid is switched from one operating mode of energy flow to another.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• ETRI Journal
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• v.26 no.3
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• pp.265-268
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• 2004

We develop a simple exact solution method for return link capacity allocation scheduling in a satellite interactive network using a hybrid code-division multiple access / time-division multiple access (CDMA/TDMA) scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.27-34
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• 2016

In the train system, railway vehicles are connected in a line. Therefore, this feature should be considered in composing network topology in a train system. Besides, inter-car communication should be distinguished from in-car communication. As for the inter-car communication, the hybrid topology was proposed to use rather than the conventional ring, star, daisy-chain, and bus topologies. In the hybrid topology, a number of cars are bound to be a group. Then star topology is used for the communication in a group and daisy-chain topology is used for the communication between groups. Hybrid topology takes the virtue of both star and daisy-chain topologies. Hence it maintains communication speed with reducing the number of connecting cables between cars. Therefore, it is important to choose the number of cars in a group to obtain higher performance. In this paper, we focus on the optimization of hybrid topology for railway cars. We first assume that the size of data and the frequency of data production for each car is identical. We also assume that the importance for the maximum number of cables to connect cars is variable as well as the importance of the communication speed. Separated weights are granted to both importance and we derive the optimum number of cars in a group for various number of cars and weights.