• Smart Structures and Systems
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• v.4 no.6
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• pp.809-828
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• 2008

Real-time hybrid testing is an attractive method to evaluate the response of structures under earthquake loads. The method is a variation of the pseudodynamic testing technique in which the experiment is executed in real time, thus allowing investigation of structural systems with time-dependent components. Real-time hybrid testing is challenging because it requires performance of all calculations, application of displacements, and acquisition of measured forces, within a very small increment of time. Furthermore, unless appropriate compensation for time delays and actuator time lag is implemented, stability problems are likely to occur during the experiment. This paper presents an approach for real-time hybrid testing in which time delay/lag compensation is implemented using model-based response prediction. The efficacy of the proposed strategy is verified by conducting substructure real-time hybrid testing of a steel frame under earthquake loads. For the initial set of experiments, a specimen with linear-elastic behavior is used. Experimental results agree well with the analytical solution and show that the proposed approach and testing system are capable of achieving a time-scale expansion factor of one (i.e., real time). Additionally, the proposed method allows accurate testing of structures with larger frequencies than when using conventional time delay compensation methods, thus extending the capabilities of the real-time hybrid testing technique. The method is then used to test a structure with a rate-dependent energy dissipation device, a magnetorheological damper. Results show good agreement with the predicted responses, demonstrating the effectiveness of the method to test rate-dependent components.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.100-100
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• 2023

단기 강우 예측에는 주로 물리과정 기반 수치예보모델(NWPs, Numerical Prediction Models) 과 레이더 기반 확률론적 방법이 사용되어 왔으며, 최근에는 머신러닝을 이용한 레이더 기반 강우예측 모델이 단기 강우 예측에 뛰어난 성능을 보이는 것을 확인하여 관련 연구가 활발히 진행되고 있다. 하지만 머신러닝 기반 모델은 예측 선행시간 증가 시 성능이 크게 저하되며, 또한 대기의 물리적 과정을 고려하지 않는 Black-box 모델이라는 한계점이 존재한다. 본 연구에서는 이러한 한계를 극복하기 위해 머신러닝 기반 blending 기법을 통해 물리과정 기반 수치예보모델인 Weather Research and Forecasting (WRF)와 최신 머신러닝 기법 (cGAN, conditional Generative Adversarial Network) 기반 모델을 결합한 Hybrid 강우예측모델을 개발하고자 하였다. cGAN 기반 모델 개발을 위해 1시간 단위 1km 공간해상도의 레이더 반사도, WRF 모델로부터 산출된 기상 자료(온도, 풍속 등), 유역관련 정보(DEM, 토지피복 등)를 입력 자료로 사용하여 모델을 학습하였으며, 모델을 통해 물리 정보 및 머신러닝 기반 강우 예측을 생성하였다. 이렇게 생성된cGAN 기반 모델 결과와 WRF 예측 결과를 결합하는 머신러닝 기반 blending 기법을 통해Hybrid 강우예측 결과를 최종적으로 도출하였다. 본 연구에서는 Hybrid 강우예측 모델의 성능을 평가하기 위해 수도권 및 안동댐 유역에서 발생한 호우 사례를 기반으로 최대 선행시간 6시간까지 모델 예측 결과를 분석하였다. 이를 통해 물리과정 기반 모델과 머신러닝 기반 모델을 결합하는 Hybrid 기법을 적용하여 높은 정확도와 신뢰도를 가지는 고해상도 강수 예측 자료를 생성할 수 있음을 확인하였다.

• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.507-522
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• 2015

In this study, some of the model verification results of STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model), a newly-developed hybrid watershed model, are presented for the runoff processes of nonpoint source pollution. For verification study of STREAM, the model was applied to a test watershed and a sensitivity analysis was also carried out for selected parameters. STREAM was applied to the Mankyung River Watershed to review the applicability of the model in the course of model calibration and validation against the stream flow discharge, suspended sediment discharge and some water quality items (TOC, TN, TP) measured at the watershed outlet. The model setup, simulation and data I/O modules worked as designed and both of the calibration and validation results showed good agreement between the simulated and the measured data sets: NSE over 0.7 and $R^2$ greater than 0.8. The simulation results also include the spatial distribution of runoff processes and watershed mass balance at the watershed scale. Additionally, the irrigation process of the model was examined in detail at reservoirs and paddy fields.

• 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.

• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.446-451
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• 1999

• 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.

• 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.