• Journal of Computing Science and Engineering
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• v.3 no.2
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• pp.73-87
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• 2009

A hybrid system is a dynamical system in which states can be changed continuously and discretely. Simulation based on numerical methods is the widely used technique for analyzing complicated hybrid systems. Numerical simulation of hybrid systems, however, is subject to two types of numerical errors: truncation error and round-off error. The effect of such errors can make an impossible transition step to become possible during simulation, and thus, to generate a simulation behavior that is not allowed by the model. The possibility of an incorrect simulation behavior reduces con.dence in simulation-based analysis since it is impossible to know whether a particular simulation trace is allowed by the model or not. To address this problem, we define the notion of Instrumented Hybrid Automata (IHA), which considers the effect of accumulated numerical errors on discrete transition steps. We then show how to convert Hybrid Automata (HA) to IRA and prove that every simulation behavior of IHA preserves the discrete transition steps of some behavior in HA; that is, simulation of IHA is sound with respect to HA.

• Computational Structural Engineering
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• v.9 no.2
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• pp.163-171
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• 1996

A hybrid knowledge model which amalgamates an object-oriented modeling approach and logic programming implementation is presented for structural health monitoring and diagnosis of instrumented structures. Domain knowledge in structural monitoring and diagnosis is formalized and represented in a logic-based object-oriented modeling environment. The model and environment have been implemented and illustrated in the context of a laboratory case study of damage detection in a successively damaged steel structure.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Korean Journal of Materials Research
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• v.21 no.2
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• pp.100-105
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• 2011

The relative viscosity was measured at different filler loadings for a cycloaliphatic epoxy resin and hexahydro-4-methylphthalic anhydride hardener system filled with micro/nano hybrid silica. Various empirical models were fitted to the experimental data and a fitting parameter such as critical filler fractions (${\phi}_{max}$) was estimated. Among the models, the Zhang-Evans model gave the best fit to the viscosity data. For all the silica loadings used, ln (relative viscosity) varied linearly with filler loadings. Using the Zhang-Evans model and the linearity characteristics of the viscosity change, simple methods to predict the relative viscosity below ${\phi}_{max}$ are presented in this work. The predicted viscosity values from the two methods at hybrid silica fractions of $\phi$ = 0.086 and 0.1506 were confirmed for a micro:nano = 1:1 hybrid filler. As a result, the difference between measured and predicted values was less than 11%, indicating that the proposed predicting methods are in good agreement with the experiment.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.4
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• pp.253-259
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• 2018

Structure behaviors resulting from an earthquake are experimentally simulated mainly through a shaking table test. As for large-scale structures, however, size effects over a miniature may make it difficult to assess actual behaviors properly. To address this problem, research on the hybrid simulation is being conducted actively. This method is to implement numerical analysis on framework members that affect the general behavior of the structure dominantly through an actual scale experiment and on the rest parts by applying the substructuring technique. However, existing studies on hybrid simulation focus mainly on Slow experimental methods, which are disadvantageous in that it is unable to assess behaviors close to the actual level if material properties change depending on the speed or the influence of inertial force is significant. The present study aims to establish a Real-time hybrid simulation system capable of excitation based on the actual time history and to verify its performance and applicability. The hybrid simulation system built up in this study utilizes the ATS Compensator system, CR integrator, etc. in order to make the target displacement the same with the measured displacement on the basis of MATLAB/Simulink. The target structure was a 2-span bridge and an RC pier to support it was produced as an experimental model in order for the shaking table test and Slow and Real-time hybrid simulations. Behaviors that result from the earthquake of El Centro were examined, and the results were analyzed comparatively. In comparison with the results of the shaking table test, the Real-time hybrid simulation produced more similar maximum displacement and vibration behaviors than the Slow hybrid simulation. Hence, it is thought that the Real-time hybrid simulation proposed in this study can be utilized usefully in seismic capacity assessment of structural systems such as RC pier that are highly non-linear and time-dependent.

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.89-102
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• 2006

The purpose of this paper is to develop a dynamic OD estimating model to overcome the limitation of depicting teal situations in dynamic simulation models based on static OD trip. To estimate dynamic OD matrix we used the hybrid discrete choice model(called the 'Demand Simulation Model'), which combines travel departure time with travel mode and travel path. Using this Demand Simulation Model, we deduced that the traveler chooses the departure time and mode simultaneously, and then choose his/her travel path over the given situation In this paper. we developed a hybrid simulation model by joining a demand simulation model and the supply simulation model (called LiCROSIM-P) which was Previously developed. We simulated the hybrid simulation model for dependent/independent networks which have two origins and one destination. The simulation results showed that AGtt(Average gap expected travel time and simulated travel time) did not converge, but average schedule delay gap converged to a stable state in transportation network consisted of multiple origins and destinations, multiple paths, freeways and some intersections controlled by signal. We present that the hybrid simulation model can estimate dynamic OD and analyze the effectiveness by changing the attributes or the traveler and networks. Thus, the hybrid simulation model can analyze the effectiveness that reflects changing departure times, travel modes and travel paths by demand management Policy, changing network facilities, traffic information supplies. and so on.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.557-562
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• 2006

In order to predict the dispersion of suspended sediment arising from dredging operation in port and navigation channel, a hybrid model for dispersive transport of turbidity plume was developed using Lee's(1998) hybrid method. Using hybrid modeling scheme advection-diffusion equation was solved by the forward particle-tracking method for advection process and by the fixed Eulerian grid method for diffusion process. To examine numerical model simulation in accuracy, the simulated results for 1-D, 2-D, and 3-D cases were compared with the analytical solutions including Kuo, et al's (1985) 3-D mathematical model. The model results were in a good agreement with the analytical solutions and mathematical model for the dispersion of turbidity plume.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.03a
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• pp.235-246
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• 1997

Hybrid fuzzy gain scheduling controller is composed of a PD control and a fuzzy control for taking the advantage of each scheme. The key structure of the hybrid fuzzy gain scheduling control scheme is so called a switch which calculates weighting values between the fuzzy controller and the PD controller. However, due to the requirement of the switch , the hybrid fuzzy gain scheduling control scheme needs extra fuzzy logic processing, thus the structure is complicated. and requires more calculation time. To eliminate the drawbacks, a new hybrid fuzzy gain scheduling control scheme is proposed in this paper. In the proposed scheme, the membership function, for calculating of weithting value, and the input and output membership functions are combined. Thus the proposed hybrid scheme does not require switch for calculation of weighting value, and as a result, the calculation time is faster and the structure is more simple than the existing hybrid controller. Computer simulation results for an inverted pendulum model under Pole-Placement PID controller, fuzzy gain scheduling controller,existing hybrid controller , and proposed hybrid controller are compared to demonstrate the good property of the proposed hybrid controller.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07a
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• pp.115-118
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• 2002

This paper presents the design method and performance characteristics of a chip-type quadrature hybrid using LTCC-MLC technology. The design method for a chip-type quadrature hybrid is based on lumped element equivalent circuit of quarter-wave transformer. The chip-type quadrature hybrid was miniaturized to a greater extent using multilayer structure and lumped element. The proposed design method can also reduce the undesirable parasitic effects of the chip-type quadrature hybrid. The proposed chip-type quadrature hybrid was designed and fabricated using the proposed design method and the equivalent circuit model of a quarter-wave transformer. Fabrication and measurement of designed chip-type quadrature hybrid show much smaller size than a conventional distributed quadrature hybrid and a good agreement with simulated results.