• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.179-187
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• 2005

The usage of mixed 2D and 3D CAD data of commercial CAD systems is required in the construction practice. Sometimes 3D wireframe model is required by end-users when 2D CAD data is delivered. However, current KOSDIC can not represent 3D CAD data, because it has been developed as a 2D drawing delivery standard. Therefore, this study is to provide exchange and sharing of mixed 2D and 3D CAD data that add 3D wireframe model in the KOSDIC. To achieve this purpose, the authors have investigated the 3D CAD entities of commercial CAD systems, and have analyzed STEP standards providing 3D wireframe model. The result, the authors have extracted 3D CAD common entities based wireframe model which shall be added in the KOSDIC. This study can be beneficial by using the developed data model for heterogeneous CAD systems, and by providing the representation of mixed 2D and 3D CAD data in construction practice such as GIS, piping system, and so forth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1341-1368
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• 2024

The concept of privacy-preserving collaborative filtering (PPCF) has been gaining significant attention. Due to the fact that model-based recommendation methods with privacy are more efficient online, privacy-preserving memory-based scheme should be avoided in favor of model-based recommendation methods with privacy. Several studies in the current literature have examined ant colony clustering algorithms that are based on non-privacy collaborative filtering schemes. Nevertheless, the literature does not contain any studies that consider privacy in the context of ant colony clustering-based CF schema. This study employed the ant colony clustering model-based PPCF scheme. Attacks like shilling or profile injection could potentially be successful against privacy-preserving model-based collaborative filtering techniques. Afterwards, the scheme's robustness was assessed by conducting a shilling attack using six different attack models. We utilize masked data-based profile injection attacks against a privacy-preserving ant colony clustering-based prediction algorithm. Subsequently, we conduct extensive experiments utilizing authentic data to assess its robustness against profile injection attacks. In addition, we evaluate the resilience of the ant colony clustering model-based PPCF against shilling attacks by comparing it to established PPCF memory and model-based prediction techniques. The empirical findings indicate that push attack models exerted a substantial influence on the predictions, whereas nuke attack models demonstrated limited efficacy.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.262-273
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• 2005

For CAD data users, few things are as frustrating as receiving CAD data that is unusable due to poor data quality. Users waste time trying to get better data, fixing the data, or even rebuilding the data from scratch from paper drawings or other sources. Most related works and commercial tools handle the boundary representation (B-Rep) shape of CAD models. However, we propose a design history?based approach for healing CAD model errors. Because the design history, which covers the features, the history tree, the parameterization data and constraints, reflects the design intent, CAD model errors can be healed by an interdependency analysis of the feature commands or of the parametric data of each feature command, and by the reconstruction of these feature commands through the rule-based reasoning of an expert system. Unlike other B Rep correction methods, our method automatically heals parametric feature models without translating them to a B-Rep shape, and it also preserves engineering information.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.30-36
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• 2004

The exchange of model design data among heterogeneous CAD systems is very difficult because each CAD system has different data structures suitable for its own functions. STEP represents product information in a common computer-interpretable form that is required to remain complete and consistent when the product information is needed to be exchanged among different computer systems. However, STEP has complex architecture to represent point, line, curve and vectors of element. Moreover it can't represent geometry data of feature based models. In this study, a structure of XML document that represents geometry data of feature based models as neutral format has been developed. To use the developed XML document, a converter also has been developed to exchange modules so that it can exchange feature based data models among heterogeneous CAD systems. Developed XML document and Converter have been applied to commercial CAD systems.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.666-668
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• 1998

In this paper we illustrate the design of a node label data flow machine based on self-timed paradigm. Data flow machines differ from most other parallel architectures, they are based on the concept of the data-driven computation model instead of the program store computation model. Since the data-driven computation model provides the excution of instructions asynchronously, it is natural to implement a data flow machine using self timed circuits.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• The Journal of Information Systems
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• v.32 no.1
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• pp.1-21
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• 2023

Purpose The purpose of this study is to investigate the intention of early application users in consideration of the characteristics of digital finance-based MyData service users. It is expected that user characteristics will affect the intention to use MyData service, which has not yet been advanced, and accordingly, it will examine how the characteristics of the initial users of MyData service and the intention to use it are connected. Design/methodology/approach The model used in this study is a value-based adoption model (VAM), and a lot of research has been conducted on information technology and online user acceptance and continuous use intention of online users. VAM has been proven useful through empirical analysis in many studies. The value-based acceptance model is a method of analyzing the intention to use Benefits and Sacrifices as the main elements of perceived value. It can be said to be a model that can analyze the benefits of use and the sacrifices to be made. Findings According to the analysis results of this study, it was found that usefulness, enjoyment, and reliability, which are the benefits of MyData service apps, had a positive effect on perceived value, which is partially consistent with existing research results. However, it was found that complexity, which is the sacrifice of MyData service apps, negatively affects perceived value and security has no negative impact. The results of security are considered to be complementary to financial institutions because MyData service deals with financial data based on personal information, and the research hypothesis is rejected because users' demands are relatively low. Therefore, MyData service apps should do more to increase benefits (usefulness, enjoyment, and reliability) than to reduce sacrifice (complexity) to users.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.6
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• pp.545-553
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• 2021

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.