• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.47-53
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• 2014

The paper investigates the limit state of deflection for short and medium span steel girder bridges. Deflection depends on stiffness of steel girders and integrity of the reinforced concrete slab (composite action). Load and resistance parameters are treated as random variables. A probabilistic model is developed for prediction of the deflection. The structural performance can be affected by deterioration of components, in particular corrosion of steel girders. In addition, the creep of concrete can greatly influence the deflection of composite structures. Therefore, the statistical models for creep and corrosion of structural steel are incorporated in the model. Structures designed according to the AASHTO LRFD Code are considered. Load and resistance models are developed to account for time-variability of the parameters. Monte Carlo simulations are used to estimate the deflections and probabilities of serviceability failure. Different span lengths and girder spacing are considered for structures designed as moment-controlled and deflection-controlled. A summary of obtained results is presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• Journal of Ocean Engineering and Technology
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• v.30 no.6
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• pp.451-457
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• 2016

For a carferry with a displacement of 1,633 tonf, a seakeeping analysis-based direct load approach (DLA) was used in Part I of these series, where the final deliverable was the long-term probabilistic acceleration components. In Part II of these series, the tangential acceleration components are explained based on two approaches: a standard called the IMO CSS code and simple formulas with the probable maximum roll and pitch rotations. The subsequent tangential acceleration-induced external force components are also introduced for these two approaches. The lashing strength components were selected from the IMO CSS code. It was assumed that two different vehicles (a car and a truck) were stowed at the most distant locations on the main deck to assume the largest tangential acceleration components and were secured with four steel wires with longitudinal and transverse lashing angles of $45^{\circ}$. Four cases were considered, with different methods for predicting the acceleration components and different tools for the external loads and lashing strengths involved: cases Rule-LS (rule-based maximum probable roll and pitch angles for predicting the acceleration components in conjunction with LashingSafety), DLA-LS (seakeeping-based long-term acceleration components with LashingSafety), CSS-LC (IMO CSS code-based acceleration components using LashCon), and CSS-LS (IMO CSS code-based acceleration components using LashingSafety). In terms of the acceleration and external force components, the CSS-LC and CSS-LS results are more than two times the results of Rule-LS. Thus, when the external forces and lashing strengths are evaluated using CSS-LC and CSS-LS, the truck needs more lashing wires, while Rule-LS and DLA-LS predict that the present lashing configuration is on the safe side.

• Convergence Security Journal
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• v.8 no.4
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• pp.173-181
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• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.404-409
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• 2008

This paper consists largely of two parts: the first part introduces the revised railway human reliability analysis (R-HRA) method which is to be used under the railway risk assessment framework, and the second part presents the features of a computer software which was developed for aiding the R-HRA process. The revised R-HRA method supplements the original R-HRA method by providing a specific task analysis guideline and a classification of performance shaping factors (PSFs) to support a consistent analysis between analysts. The R-HRA software aids the analysts in gathering information for HRA, qualitative error prediction including identification of external error modes and internal error modes, quantification of human error probability, and reporting the overall analysis results. The revised R-HRA method and software are expected to support the analysts in an effective and efficient way in analysing human error potential in railway event or accident scenarios.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.39-39
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• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.243-243
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• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.

• Composites Research
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• v.21 no.6
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• pp.23-30
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• 2008

In this paper, the effect of the natural aging on the strength distribution and structural service life of a Filament Wound (FW) composite pressure vessel was studied. The fiber failure strain, which is varied significantly, was considered as the design random variable and the strength analysis was carried out by probabilistic numerical approach. The progressive failure analysis technique and the First Order Reliability Method (FORM) were embedded in this numerical model. As the calculation results, the probability of failure was obtained for each aging time steps and it is found that the strength degradation in FW composite pressure vessel, due to the natural aging, appears within 10 year-aging-time. As an example of the life prediction under natural aging using arbitrary laminated model, the service lifetime of 13 years was predicted based on the probability of failure of 2.5% and the design pressure of 3,250 psi.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.880-885
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• 2024

For Safety Assisted Engineering works, real-time simulators have emerged as a mandatory tool among all the key actors involved in the nuclear industry (utilities, designers and safety authorities). EDF, Electricité de France, as the leading worldwide nuclear power plant operator, has a crucial need for efficient and updated simulation tools for training, operating and safety analysis support. This paper will present the work performed at EDF/DT to develop a new generation of engineering simulator to fulfil these tasks. The project is called SiRENE, which is the acronym of Re-hosted Engineering Simulator in French. The project has been economically challenging. Therefore, to benefit from existing tools and experience, the SiRENE project combines: - A part of the process issued from the operating fleet training full-scope simulator. - An improvement of the simulator prediction reliability with the integration of High-Fidelity models, used in Safety Analysis. These High-Fidelity models address Nuclear Steam Supply System code, with CATHARE thermal-hydraulics system code and neutronics, with COCCINELLE code. - And taking advantage of the last generation and improvements of instructor station. The intensive and challenging uses of the new SiRENE engineering simulator are also discussed. The SiRENE simulator has to address different topics such as verification and validation of operating procedures, identification of safety paths, tests of I&C developments or modifications, tests on hydraulics system components (pump, valve etc.), support studies for Probabilistic Safety Analysis (PSA). etc. It also emerges that SiRENE simulator is a valuable tool for self-training of the newcomers in EDF nuclear engineering centers. As a modifiable tool and thanks to a skillful team managing the SiRENE project, specific and adapted modifications can be taken into account very quickly, in order to provide the best answers for our users' specific issues. Finally, the SiRENE simulator, and the associated configurations, has been distributed among the different engineering centers at EDF (DT in Lyon, DIPDE in Marseille and CNEPE in Tours). This distribution highlights a strong synergy and complementarity of the different engineering institutes at EDF, working together for a safer and a more profitable operating fleet.