• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.3
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• pp.145-152
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• 2021

KAERI has planned to carry out a series of dynamic tests using a shaking table and time-history analyses for a channel-type concrete shear wall to investigate its seismic performance because of the recently frequent occurrence of earthquakes in the south-eastern parts of Korea. The overall size of a test specimen is b×l×h =2500 mm×3500 mm×4500 mm, and it consists of three stories having slabs and walls with thicknesses of 140 mm and 150 mm, respectively. The system identification, FE model updating, and time-history analysis results for a test shear wall are presented herein. By applying the advanced system identification, so-called pLSCF, the improved modal parameters are extracted in the lower modes. Using three FE in-house packages, such as FEMtools, Ruaumoko, and VecTor4, the eigenanalyses are made for an initial FE model, resulting in consistency in eigenvalues. However, they exhibit relatively stiffer behavior, as much as 30 to 50% compared with those extracted from the test in the 1st and 2nd modes. The FE model updating is carried out to consider the 6-dofs spring stiffnesses at the wall base as major parameters by adopting a Bayesian type automatic updating algorithm to minimize the residuals in modal parameters. The updating results indicate that the highest sensitivity is apparent in the vertical translational springs at few locations ranging from 300 to 500% in variation. However, their changes seem to have no physical meaning because of the numerical values. Finally, using the updated FE model, the time-history responses are predicted by Ruaumoko at each floor where accelerometers are located. The accelerograms between test and analysis show an acceptable match in terms of maximum and minimum values. However, the magnitudes and patterns of floor response spectra seem somewhat different because of the slightly different input accelerograms and damping ratios involved.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.181-193
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• 2022

This paper seeks to investigate major macroeconomic factors and bond yield interactions in Thai bond markets, with the goal of forecasting future bond yields. This study examines the best predictive yields for future bond yields at different maturities of 1-, 3-, 5-, 7-, and 10-years using time series data of economic indicators covering the period from 1998 to 2020. The empirical findings support the hypothesis that macroeconomic factors influence bond yield fluctuations. In terms of forecasting future bond yields, static predictions reveal that in most cases, the BVAR model offers the best predictivity of bond rates at various maturities. Furthermore, the BVAR model has the best performance in dynamic rolling-window, forecasting bond yields with various maturities for 2-, 4-, and 8-quarters. The findings of this study imply that the BVAR model forecasts future yields more accurately and consistently than other competitive models. Our research could help policymakers and investors predict bond yield changes, which could be important in macroeconomic policy development.

• Journal of KIISE
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• v.42 no.7
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• pp.889-900
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• 2015

In order to solve problems happening in the practical environment of complicated system, the importance of the self-adaptive system has recently begun to emerge. However, since the differences between the model built at the time of system design and the practical environment can lead the system into unpredictable situations, the study into methods of dealing with it is also emerging as an important issue. In this paper, we propose a method for deciding on the adaptation time in an uncertain environment, and reflecting the real-time environment in the system's model. The proposed method calculates the Bayesian Surprise for the suitable adaptation time by comparing previous and current states, and then reflects the result following the performed policy in the design model to help in deciding the proper policy for the actual environment. The suggested method is applied to a navigation system to confirm its effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• IE interfaces
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• v.10 no.1
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• pp.47-55
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• 1997

This paper deals with an assembly process of the TFT glasses and the color filters in LCD manufacturing. Two specific problems are presented and solved. One is a matching problem to find the best matches between a set of TFT glasses and a set of color filters, which result in the maximum number of good LCD assemblies. A simple mathematical model is constructed for this problem and an optimal solution can be obtained using an existing algorithm. The other is a main problem that requires a determination of an economic lot size of the color filters which are going to be assembled with a given set of TFT glasses. A Bayesian dynamic forecasting model is developed to predict the defective patterns of color filters. Based on the predicted defective rate of color filters, the minimum lot size of the color filters can be determined to minimize the probability of losing good TFT glasses and color filters.

• Computers and Concrete
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• v.29 no.1
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• pp.59-67
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• 2022

In this paper, a reliability-based approach has been implemented to develop seismic analytical fragility curves of highway bridges. A typical bridge class of the Central and South-eastern United States (CSUS) region was selected. Detailed finite element modelling is presented and Incremental Dynamic Analysis (IDA) is used to capture the behavior of the bridge from linear to nonlinear behavior. Bayesian linear regression method is used to define the demand model. A reliability approach is implemented to generate the analytical fragility curves and the proposed approach is compared with the conventional fragility analysis procedure.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.906-910
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• 2008

As the mobile environment becomes widely used, there is a growth on the concern about recognizing and sharing user context. Sharing context makes the interaction between human more plentiful as well as helps to keep a good social relationship. Recently, it has been applied to some messengers or mobile applications with sharing simple contexts, but it is still required to recognize and share more complex and diverse contexts. In this paper, we propose a context-aware messenger that collects various sensory information, recognizes representative user contexts such as emotion, stress, and activity by using dynamic Bayesian networks, and visualizes them. It includes a modular model that is effective to recognize various contexts and displays them in the form of icons. We have verified the proposed method with the scenario evaluation and usability test.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.