• 한국지진공학회논문집
• /
• 제22권2호
• /
• pp.77-86
• /
• 2018

This study develops an empirical prediction equation of spectral acceleration responses of earthquakes which can induce structural damages. Ground motion records representing hazards of low-to-moderate seismic regions were selected and organized with several influential factors affecting the response spectra. The empirical equation and estimator coefficients for acceleration response spectra were then proposed using a robust nonlinear optimization coupled with a regression analysis. For analytical verification of the prediction equation, response spectra used for low-to-moderate seismic regions were estimated and the predicted results were comparatively evaluated with measured response spectra. As a result, the predicted shapes of response spectra can simulate the graphical shapes of measured data with high accuracy and most of predicted results are distributed inside range of correlation of variation (COV) of 30% from perfectly correlated lines.

• 한국항해항만학회지
• /
• 제35권9호
• /
• pp.735-740
• /
• 2011

It is necessary to develop the one stop system in order to protect our marine environment rapidly from oil spill accident. The purpose of this study is to develop real time database for oil spill prediction modeling and implement real time prediction modelling with ESI and server-client GIS based user interface. The existing oil spill prediction model cannot provide one stop information system for public and government who should protect sea from oil spill accident. The development of multi user based information system permits integrated handling of real time meteorological data from external ftp. A server-client GIS based model is integrated on the basis of real time database and ESI map to provide the result of the oil spill prediction model. End users can access through the client interface and request analysis such as oil spill prediction and GIS functions on the network as their own purpose.

• Journal of Computing Science and Engineering
• /
• 제11권3호
• /
• pp.100-108
• /
• 2017

In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

• Communications for Statistical Applications and Methods
• /
• 제5권3호
• /
• pp.607-621
• /
• 1998

When fitting a response surface model, the least squares estimates of the model's parameters and the prediction variance will generally depend on how the response surface design is blocked. That is, the choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of the prediction variance even if the experimental runs are the same. Therefore, care should be exercised in the selection of blocks. In this paper, we prognose a graphical method for evaluating the effect of blocking in a response surface designs using cuboidal regions in the presence of a fixed block effect. This graphical method can be used to investigate how the blocking has influence on the prediction variance throughout the entire experimental region of interest when this region is cuboidal, and compare the block effect in the cases of the orthogonal and non-orthogonalblockdesigns, resfectively.

• Structural Engineering and Mechanics
• /
• 제83권3호
• /
• pp.401-413
• /
• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• 한국소음진동공학회논문집
• /
• 제14권1호
• /
• pp.40-49
• /
• 2004

In this paper, vibration response of aerial gas pipeline due to vehicle loads was quantitatively estimated through experiment and analysis in open cut construction site. The vehicle vibration of various construction machines causes serious effect to the aerial gas pipeline. The new vibration prediction equations presented in this study can estimate the vibration velocity response of the aerial gas pipeline. In the nitration prediction equations, the vehicle′s weight and traveling velocity, which are the sources of vibration, are combined into the term called, "scaled weight" Methods to reduce vibration were proposed in case the vibration velocity response of the gas pipeline exceeded the vibration criterion, using the vibration prediction equations presented in this study. One was to limit the vehicle′s traveling velocity and the other to install the isolation equipment. Both methods can be estimated quantitatively.

• Wind and Structures
• /
• 제29권2호
• /
• pp.129-137
• /
• 2019

The maximum along-wind displacement of a considerable amount of building under simulated wind loads is computed with the aim to produce a simple prediction model using multiple regression analysis with variables transformation. The Shinozuka and Newmark methods are used to simulate the turbulent wind and to calculate the dynamic response, respectively. In order to evaluate the prediction performance of the regression model with longer degree of determination, two complex structural models were analyzed dynamically. In addition, the prediction model proposed is used to estimate and compare the maximum response of two test buildings studied with wind loads by other authors. Finally, it was proved that the prediction model is reliable to estimate the maximum displacements of structures subjected to the wind loads.

• Structural Engineering and Mechanics
• /
• 제69권6호
• /
• pp.677-691
• /
• 2019

In recent years, interest is growing in the engineering community on the experimental assessment and the theoretical prediction of the out-of-plane (OOP) seismic response of unreinforced masonry (URM) infills, which are widespread in Reinforced Concrete (RC) buildings in Europe and in the Mediterranean area. In the literature, some mechanical-based models for the prediction of the entire OOP force-displacement response have been formulated and proposed. However, the small number of experimental tests currently available has not allowed, up to current times, a robust and reliable evaluation of the predictive capacity of such response models. To enrich the currently available experimental database, six pure OOP tests on URM infills in RC frames were carried out at the Department of Structures for Engineering and Architecture of the University of Naples Federico II. Test specimens were built with the same materials and were different only for the thickness of the infill walls and for the number of their edges mortared to the confining elements of the RC frames. In this paper, the results of these experimental tests are briefly recalled. The main aim of this study is comparing the experimental response of test specimens with the prediction of mechanical models presented in the literature, in order to assess their effectiveness and contribute to the definition of a robust and reliable model for the evaluation of the OOP seismic response of URM infill walls.

• 품질경영학회지
• /
• 제27권1호
• /
• pp.59-79
• /
• 1999

The fitting of a response surface model and the subsequent exploration of the response surface are usually based on the assumption that the experimental runs are carried out under homogeneous conditions. This, however, may be quite often difficult to achieve in many experiments. To control such an extraneous source of variation, the response surface design should be arranged in several blocks within which homogeneity of conditions can be maintained. In this case, when fitting a response surface model, the least squares estimates of the model's parameters and the prediction variance will generally depend on how the response surface design is blocked. That is, the choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of the prediction variance. In this paper, we propose a measure for evaluating the effect of blocking of response surface designs using cuboidal regions.

• Genomics & Informatics
• /
• 제19권1호
• /
• pp.10.1-10.7
• /
• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.