• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.3
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• pp.73-84
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• 2010

This study presents a novel method to identify hazardous segments of freeway using global positioning system(GPS) based probe vehicle data. A variety of candidate contributing factors leading to higher potential of accident occurrence were extracted from the probe vehicle dataset. The research problem was defined as a classification problem, then a well-known classifier, bayesian neural network was adopted to solve the problem. A binary logistic regression technique was also used for selecting salient input variables. Test results showed that the proposed method is promising in extracting hazardous freeway sections. The outcome of this study will be effectively used for evaluating the safety of freeway sections and deriving countermeasures to prevent accidents.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.381-389
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• 2012

This paper proposes a method which corrects location data of GPS for navigation of outdoor mobile robot. The method uses a Bayesian filter approach called the particle filter(PF). The method iterates two procedures: prediction and correction. The prediction procedure calculates robot location based on translational and rotational velocity data given by the robot command. It incorporates uncertainty into the predicted robot location by adding uncertainty to translational and rotational velocity command. Using the sensor characteristics of the GPS, the belief that a particle assumes true location of the robot is calculated. The resampling from the particles based on the belief constitutes the correction procedure. Since usual GPS data includes abrupt and random noise, the robot motion command based on the GPS data suffers from sudden and unexpected change, resulting in jerky robot motion. The PF reduces corruption on the GPS data and prevents unexpected location error. The proposed method is used for navigation of a mobile robot in the 2011 Robot Outdoor Navigation Competition, which was held at Gwangju on the 16-th August 2011. The method restricted the robot location error below 0.5m along the navigation of 300m length.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• KDI Journal of Economic Policy
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• v.36 no.3
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• pp.1-23
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• 2014

This paper has studied the monetary policy in Korea with a time varying VAR model using four key macroeconomic variables. First, inclusion of the exchange rate was a crucial factor in evaluating Korean monetary policy since the monetary policy demonstrated sensitivity to exchange rate movements during the crisis periods of both the Asian financial crisis of 1997 and the global financial crisis of 2008. Second, a specification of the stochastic volatilities in TVP-VAR model is important in explaining excessive movements of all variables in the sample. The overall moderation of variables in 2000s was more or less due to a reduction of the stochastic volatilities but also somewhat due to the macroeconomic fundamental structures captured by impulse response functons. Third, the degree of the monetary policy effectiveness of inflation was mitigated in recent periods but with increased persistence. Lastly, the monetary policy stance towards inflation stabilization has advanced ever since the inflation targeting scheme was adopted. However, there still seems to be a room for improvement in this aspect since the degree of the monetary policy stance towards inflation stabilization was relatively weaker than to output stabilization.

• Korean Journal of Construction Engineering and Management
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• v.19 no.3
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• pp.33-42
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• 2018

In the aftermath of an earthquake, seismic-damaged infrastructure systems loss estimation is the first step for the disaster response. However, lifeline systems' ability to supply service can be volatile by external factors such as disturbances of nearby facilities, and not by own physical issue. Thus, this research develops the bayesian model for probabilistic inference on common-cause and cascading failure of seismic-damaged lifeline systems. In addition, the authors present network robustness estimation metrics in the context of failure propagation. In order to quantify the functional loss and observe the effect of the mitigation plan, power and water supply system in Daegu-Gyeongbuk in South Korea is selected as case network. The simulation results show that reduction of cascading failure probability allows withstanding the external disruptions from a perspective of the robustness improvement. This research enhances the comprehensive understanding of how a single failure propagates to whole lifeline system performance and affected region after an earthquake.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1269-1276
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• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• Journal of KIISE
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• v.43 no.7
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• pp.795-800
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• 2016

In image classification, recent CNNs compete with human performance. However, there are limitations in more general recognition. Herein we deal with indoor images that contain too much information to be directly processed and require information reduction before recognition. To reduce the amount of data processing, typically variational inference or variational Bayesian methods are suggested for object detection. However, these methods suffer from the difficulty of marginalizing over the given space. In this study, we propose an image-text integrated recognition system using active vision based on Spatial Transformer Networks. The system attempts to efficiently sample a partial region of a given image for a given language information. Our experimental results demonstrate a significant improvement over traditional approaches. We also discuss the results of qualitative analysis of sampled images, model characteristics, and its limitations.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1112-1123
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• 2003

WSD(word sense disambiguation) is one of the most difficult problems in Korean information processing. The Bayesian model that used semantic information, extracted from definition corpus(1 million POS-tagged eojeol, Korean dictionary definitions), resulted in accuracy of 72.08% (nouns 78.12%, verbs 62.45%). This paper proposes the statistical WSD model using NPH(New Prior Probability of Homonym sense) and distance weights. We select 46 homonyms(30 nouns, 16 verbs) occurred high frequency in definition corpus, and then we experiment the model on 47,977 contexts from ‘21C Sejong Corpus’(3.5 million POS-tagged eojeol). The WSD model using NPH improves on accuracy to average 1.70% and the one using NPH and distance weights improves to 2.01%.