• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Fire Science and Engineering
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• v.31 no.1
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• pp.26-35
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• 2017

Since the Northridge earthquake (1994) and Kobe earthquake (1995), the concept of performance-based design has been actively introduced to design major structures and buildings. Recently, the seismic design code was established for fire protection facilities. Therefore, the important fire protection facilities should be designed and constructed according to the seismic design code. Accordingly, uniform hazard spectra (UHS), with annual exceedance probabilities, corresponding to the performance level, such as operational, immediate occupancy, life safety, and collapse prevention, are required for performance-based design. Using the method of probabilistic seismic hazard analysis (PSHA), the uniform hazard spectra for 5 major cities in Korea with a recurrence period of 500, 1,000, and 2,500 years corresponding to frequencies of (0.5, 1.0, 2.0, 5.0, 10.0)Hz and PGA, were analyzed. The expert panel was comprised of 10 members in seismology and tectonics. The ground motion prediction equations and several seismo tectonic models suggested by 10 expert panel members in seismology and tectonics were used as the input data for uniform hazard spectrum analysis. According to sensitivity analysis, the parameter of spectral ground motion prediction equations has a greater impact on the seismic hazard than seismotectonic models. The resulting uniform hazard spectra showed maximum values of the seismic hazard at a frequency of 10Hz and also showed the shape characteristics, which are similar to previous studies and related technical guides for nuclear facilities.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of the Korean GEO-environmental Society
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• v.15 no.11
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• pp.67-75
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• 2014

In this study, the staged seismic performance evaluations were conducted to the 91 high speed railway tunnels in use for checking whether to comply with the recent design criteria or not. In addition, the seismic fragility functions of the tunnels were developed to allow the probabilistic risk assessment. The results of the staged seismic performance evaluations which consist of a preliminary assessment and a detailed assessment, show that the tunnels comply with the recent design criteria. With reference to the results of previous studies, a form of the proposed seismic fragility functions was set as a log-normal distribution by PGA, and the parameters of the functions were determined by using the probability of damage for the design PGA level. The seismic fragility functions were developed for each types (Cut & Cover, NATM) of tunnels. The seismic fragility functions from this study and the existing research results (FEMA, 2004) were compared to evaluate the seismic performance level of the tunnels, as a result the tunnels of this study were relatively superior to the ASSM tunnels on the seismic performance.

• Composites Research
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• v.32 no.6
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• pp.327-334
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• 2019

The delamination is a special mode of failure occurring in composite laminates. Several numerical studies with finite element analysis have been carried out on the delamination behavior of unidirectional composite laminates. On the other hand, the fracture for the multi-directional composite laminates may occur not only along the resin-fiber interface between plies known as interply or interlaminar fracture but also within a ply known as interyarn or intralaminar fracture accompanied by matrix cracking and fiber bridging. In addition, interlaminar and intralaminar cracks appear at irregular proportions and intralaminar cracks proceeded at arbitrary angle. The probabilistic analysis method for the prediction of crack growth behavior within a layer is more advantageous than the deterministic analysis method. In this paper, we analyze the crack path when the mode I load is applied to the cross-ply carbon/epoxy composite laminates and collect and analyze the probability data to be used as the basis of the probabilistic analysis in the future. Two criteria for the theoretical analysis of the crack growth direction were proposed by analyzing the stress field at the crack tip of orthotropic materials. Using the proposed method, the crack growth directions of the cross-ply carbon/epoxy laminates were analyzed qualitatively and quantitatively and compared with experimental results.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.435-446
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• 2014

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966~2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.117-124
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• 2013

Association rule of data mining techniques is the method to quantify the relationship between a set of items in a huge database, andhas been applied in various fields like internet shopping mall, healthcare, insurance, and education. There are three primary interestingness measures for association rule, support and confidence and lift. Confidence is the most important measure of these measures, and we generate some association rules using confidence. But it is an asymmetric measure and has only positive value. So we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure (PIM) with all marginal proportions (AMP) to solve this problem. The comparative studies with support, confidences, lift, chi-square statistics, and some similarity measures by PIM with AMPare shown by numerical example. As the result, we knew that the similarity measures by PIM with AMP could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values, and select the best similarity measure by PIM with AMP.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.4
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• pp.455-472
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• 2018

It is important to study how to manage dry storage casks of spent nuclear fuels (SNF), because wet storage spaces for SNF will shortly be at full capacity in the Republic of Korea. The US has operated a dry storage cask system for several decades, and has carried out significant studies into how to successfully manage dry storage cask for SNF. This type of expertise and experience is currently lacking in the Republic of Korea. The degradation of dry casks is an important issue that must be considered. In particular, chloride-induced stress corrosion cracking (CISCC) is known to lead to the release of radioisotopes from canisters. The U.S. Department of Energy, U.S. Nuclear Regulatory Commission, and the Electric Power Research Institute have undertaken research into the CISCC mechanism. In addition, Sandia National Laboratories (SNL) has extensively researched CISCC and how to manage it in dry storage canisters. In this review paper, the probabilistic model proposed by the SNL is analyzed and, based on this model, US-based CISCC research is reviewed in detail. This paper will inform the management of dry cask storage of SNF from light water reactors in austenite stainless steel canisters in the Republic of Korea.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.252-260
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• 2022

Purpose: As the intensity and frequency of natural hazards are increasing due to climate change, external events that affecting nuclear power plants(NPPs) may increase. NPPs must be protected from external events such as natural hazards and human-induced hazards. External events that may occur in NPPs should be identified, and external events that may affect NPPs should be identified. This study introduces the methodology of identification and screening methods for external events by literature review. Method: The literature survey was conducted on the identification and screening methods of external events for probabilistic safety assessment of NPPs. In addition, the regulations on the identification and screening of external events were investigated. Result: In order to minimize the cost of external event impact analysis of nuclear power plants, research on identifying and screening external events is being conducted. In general, in the identification process, all events that can occur at the NPPs are identified. In the screening process, external events are selected based on qualitative and quantitative criteria in most studies. Conclusions: The process of identifying and screening external events affecting NPPs is becoming important. This paper, summarize on how to identify and screen external events for a probabilistic safety assessment of NPPs. It is judged that research on bounding analysis and conservative analysis methods performed in the quantitative screening process of external events is necessary.