• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.1-18
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• 2023

The paper presents a Bayesian Finite element (FE) model updating methodology by utilizing modal data. The dynamic condensation technique is adopted in this work to reduce the full system model to a smaller model version such that the degrees of freedom (DOFs) in the reduced model correspond to the observed DOFs, which facilitates the model updating procedure without any mode-matching. The present work considers both the MPV and the covariance matrix of the modal parameters as the modal data. Besides, the modal data identified from multiple setups is considered for the model updating procedure, keeping in view of the realistic scenario of inability of limited number of sensors to measure the response of all the interested DOFs of a large structure. A relationship is established between the modal data and structural parameters based on the eigensystem equation through the introduction of additional uncertain parameters in the form of modal frequencies and partial mode shapes. A novel sampling strategy known as the Metropolis-within-Gibbs (MWG) sampler is proposed to sample from the posterior Probability Density Function (PDF). The effectiveness of the proposed approach is demonstrated by considering both simulated and experimental examples.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.427-442
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• 2011

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3009-3015
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• 2013

Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer and method of the most fundamental being determining an individual's genotype from multiple aligned short read sequences at a position. Bayesian algorithm estimate parameter using posterior genotype probabilities and other method, EM algorithm, estimate parameter using maximum likelihood estimate method in observed data. Here, we propose a novel genotype-calling system and compare and analyze the effect of sample size(S = 50, 100 and 500) on posterior estimate of sequencing error rate, somatic mutation status and genotype probability. The result is that estimate applying Bayesian algorithm even for 50 of small sample size approached real parameter than estimate applying EM algorithm in small sample more accurately.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.199-208
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• 2014

Purpose: This paper explains how to obtain the Bayes estimates of the whole launch vehicle and of a vehicle stage, respectively, for a newly developed expendable launch vehicle. Methods: We determine the parameters of the beta prior distribution using the upper bound of the 60% Clopper-Pearson confidence interval of failure probability which is calculated from previous launch data considering the experience of the developer. Results: Probability that a launch vehicle developed from an inexperienced developer succeeds in the first launch is obtained by about one third, which is much smaller than that estimated from the previous research. Conclusion: The proposed approach provides a more conservative estimate than the previous noninformative prior, which is more reasonable especially for the initial reliability of a new vehicle which is developed by an inexperienced developer.

• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.3-13
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• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.443-461
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• 2009

In order to overcome the lack of Korean credit rating migration data, we consider an empirical Bayes procedure to estimate credit rating migration matrices. We derive the posterior probabilities of Korean credit rating transitions by utilizing the Moody's rating migration data and the credit rating assignments from Korean rating agency as prior information and likelihood, respectively. Metrics based upon the average transition probability are developed to characterize the migration matrices and compare our Bayesian migration matrices with some given matrices. Time series data for the metrics show that our Bayesian matrices are stable, while the matrices based on Korean data have large variation in time. The bootstrap tests demonstrate that the results from the three estimation methods are significantly different and the Bayesian matrices are more affected by Korean data than the Moody's data. Finally, Monte Carlo simulations for computing the values of a portfolio and its credit VaRs are performed to compare these migration matrices.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.663-673
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• 2005

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized far this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and $100\%$ data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1 km spacing and eliminating my resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values oi mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.161-166
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• 2020

This paper suggests an extension of the unified Bayesian Tikhonov regularization method. The unified method establishes the relationship between Tikhonov regularization parameter and Bayesian hyper-parameters, and presents a formula for obtaining the regularization parameter using the maximum posterior probability and the evidence framework. When the dimension of the data matrix is m by n (m >= n), we derive that the total misfit has the range of m ± n instead of m. Thus the search range is extended from one to 2n + 1 integer points. Golden section search rather than linear one is applied to reduce the time. A new benchmark for optimizing relative error and new model selection criteria to target it are suggested. The experimental results show the effectiveness of the proposed method in the image restoration problem.