• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.8
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• pp.721-725
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• 2016

This paper reviews the analysis of a given scenario according to the Hybrid Model, and why accident causation models are necessary in casualty investigations. The given scenario has been analyzed according to the Hybrid Model using its main five components, fallible decisions, line management, psychological precursors to unsafe acts, unsafe acts, and inadequate defenses. In addition, the differences between the SHEL and the Hybrid Model, and the importance of a safety barrier during an accident investigation, are shown in this paper. One unit of SHEL can be linked with another unit of SHEL. However, it cannot be used for the analysis of an accident. Therefore, we must use an accident causation model, which can be a Hybrid Model. This can explain the "How" and "Why" of accident, so it is a suitable model for analyzing them. During an accident investigation, the reason we focus on a safety barrier is to create another safety barrier or to change an existing safety barrier if that barrier fails. Hence, the paper shows how a sea accident can be investigated, and we propose a preventive way of avoiding the accident through combining the methods of different models for the future.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.3
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• pp.389-396
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• 2008

The hybrid model can be used to predict the initial near field mixing and the far field transport of the buoyant jets, which are discharged from the submerged wastewater ocean outfall. In the near field, the jet integral model can be used for single port diffusers while the ${\sigma}$ transformed particle tracking model was used in the far field. In this study, the experimental study was performed to verify the developed hybrid model in the previous research. The developed hybrid model properly predict the surface and vertical concentration distribution of the single buoyant jets with various effluent and ambient conditions. The hybrid model can also simulate the surface concentration distribution of the rosette diffuser except for the parallel diffuser with the higher densimetric Froude number due to the assumption that dynamic effects of the effluent plumes are negligible in the far field. The application of the hybrid model to rosette diffusers can predict the concentration near the diffuser more accurately when the line-plume approximation is used.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.43-53
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• 2007

Internet becomes more and more popular, and most companies and institutes use web services for e-business and many other purposes. With the explosion of Internet, the occurrence of cyber terrorism has grown very rapidly. Simulation is one of the most widely used method to study internet worms. But, it is quite challenging to simulate very large-scale worm attacks because of various reasons. In this paper, we propose a hybrid modeling method for RCS(Random Constant Spreading) worm simulation. The proposed hybrid model simulates worm attacks by synchronizing modeling network and packet network. So, this model will be both detailed enough to generate realistic packet traffic, and efficient enough to model a worm spreading through the Internet. Moreover, our model have the capability of dynamic updates of the modeling parameters. Finally, we simulate the hybrid model with the CodeRed worm to show validity of our proposed model for RCS worm simulation.

• Journal of the Korean Society for Advanced Composite Structures
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• v.2 no.2
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• pp.1-6
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• 2011

This study aims at predicting structural behaviors of RC (Reinforced Concrete) beams retrofitted with hybrid FRPs (Fiber Reinforced Polymers). Toward this goal, structural analysis for the RC beams retrofitted with hybrid FRPs are performed and validated using existing experimental data. For the analysis, failure models due to debonding of FRPs and concrete separation are implemented within FE (Finite Element) model, based on Smith and Teng, model, and Teng and Yao model, respectively. Nonlinear material and geometrical effects are also included in the analysis. The suggested modeling approaches are able to predict structural behaviors of RC beams retrofitted with hybrid FRPs similar to the experimental data, however, a numerical model needs to be developed in order to predict failure strength of RC beams retrofitted with hybrid FRPs accurately.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.6
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• pp.187-194
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• 2005

The objective of this study is to validate the hybrid breakup model and the vaporization model for GDI spray analysis at vaporization and non-vaporization conditions. The atomization process is modeled by using hybrid breakup model that is composed of Linearized Instability Sheet Atomization (LISA) model and Aerodynamically Progressed Taylor Analogy Breakup (APTAB) model. The vaporization process is modeled by using modified Abramzon & Sirignano model. The exciplex fluorescence method was used for comparing the calculated results with the experimental ones. The experiment and the calculation were performed at the ambient pressures of 0.1 MPa, 0.5 MPa and 1.0 MPa and the ambient temperature of 293K and 473K.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• 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.

• Advances in aircraft and spacecraft science
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• v.5 no.5
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• pp.595-613
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• 2018

The objective of our study is to analyze the behavior of bonded, riveted and hybrid (bonded / riveted) steel / steel assemblies by tensile tests and to show the advantage of a hybrid assembly over other processes. the finite element method with the ABAQUS numerical code was used to model the fracture behavior of the different assemblies. Cohesive zone models (CZM) have been adopted to model crack propagation in bonded joints using a bilinear tensile separation law implemented in the ABAQUS finite element code. The riveted assemblies were modeled with the XFEM damage method identified in this ABAQUS numerical code. Both CZM and XFEM methods are combined to model hybrid assemblies. The results are consistent with the experimental results and make it possible to guarantee the validity of the applied numerical model. The use of a hybrid assembly shows a high resistance compared to other conventional methods, where the number of rivets has been highlighted. The use of the hybrid assembly improves mechanical strength and increases service life compared to a single lap joint and a riveted joint.