• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.380-386
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• 2017

Fennovoima's nuclear power plant, Hanhikivi 1, $Pyh{\ddot{a}}joki$, Finland, is currently in design phase, and its construction is scheduled to begin in 2018 and electricity production in 2024. The objective of this paper is to produce a preliminary list of safety-significant external event combinations including preliminary probability estimates, to be used in the probabilistic risk assessment of Hanhikivi 1 plant. Starting from the list of relevant single events, the relevant event combinations are identified based on seasonal variation, preconditions related to different events, and dependencies (fundamental and cascade type) between events. Using this method yields 30 relevant event combinations of two events for the Hanhikivi site. The preliminary probability of each combination is evaluated, and event combinations with extremely low probability are excluded from further analysis. Event combinations of three or more events are identified by adding possible events to the remaining combinations of two events. Finally, 10 relevant combinations of two events and three relevant combinations of three events remain. The results shall be considered preliminary and will be updated after evaluating more detailed effects of different events on plant safety.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.170-178
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• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.110-116
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• 2022

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs. Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated. In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.209-214
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• 2014

In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.19-30
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• 1986

This paper provides an evaluation of static flowshop scheduling heuristics for minimizing makespan as an objective function in the dynamic flowshop model, in which new jobs with stochastic processing times arrive at the shop randomly over time and are added into the waiting jobs for processing. A total of sixteen scheduling heuristics, including several revisions and combinations of previously reported me-sixteen scheduling heuristics, including several revisions and combinations of previously reported methods, are surmmarized. These scheduling rules are evaluated via computer using a SLAM discrete event simulation model. The results for the simulation are analyzed using both statistical and nonstatistical methods. The results from the study suggest which of the popular scheduling rules hold promise for application to practical dynamic flowshop problems.

• ETRI Journal
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• v.28 no.5
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• pp.638-647
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• 2006

This paper investigates two kinds of strategic combinations carried out by the European telecom operators between 1986 and 2001: acquisitions, on the one hand, and strategic alliances, on the other. The aim of the paper is twofold. First, it analyzes the behavior adopted by these companies to adapt to an environment that, after the processes of globalization and privatization presents a clearly different structure. Second, it focuses on the effect that internationalization and diversification could exert over the returns obtained by the European telecom firms involved in acquisitions and alliances, differentiating when such strategic combinations are more profitable for the shareholders of the firms that carry them out.

• Journal of Energy Engineering
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• v.26 no.4
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• pp.23-28
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• 2017

Hazard evaluation and FTA are performed as the first and the second step of QRA for a LNG storage tank. Hazards are identified using HAZOP. Each segment of the system is examined, and we list all possible deviations from normal operating conditions and how they might occur. The consequences on the process are assessed, and the means available to detect and correct the deviations are reviewed. The FTA is carried out to analyse the hazards identified from the HAZOP study. A top event is selected to be release of LNG. Then all combinations of individual failures that can lead to the hazardous event are shown in the logical format of the fault tree system.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.141-141
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• 2021

This study proposed an equation for Rainfall Threshold for Flood Warning (RTFW) for urban areas based on computer simulations. First, a coupled 1D-2D dual-drainage model was developed for nine watersheds in Seoul, Korea. Next, the model simulation was repeated for a total of 540 combinations of the synthetic rainfall events and watershed imperviousness (9 watersheds × 4 NRCS Curve Number (CN) values × 15 rainfall events). Then, the results of the 101 simulations with the critical flooded depth (0.25m-0.35m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation) and the watershed Curve Number. The results suggest that 1) the rainfall with greater temporal variability causes critical floods with less amount of total rainfall; and that 2) the greater imperviousness requires less rainfall to have critical floods. For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The results of the application showed high performance of the warning system in issuing the flood warning, with the hit, false and missed alarm rates at 68%, 32% and 7.4% respectively for the 2010 event and 49%, 51% and 10.7% for the event in 2011.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Wind and Structures
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• v.15 no.5
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• pp.423-439
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• 2012

Three common medium- rise building forms were physically tested to study their overall wind induced structural response. Emphasis was placed on the torsional response and its correlation with other peak responses. A higher correlation was found between the peak responses than between the general fluctuating parts of the signals. This suggests a common mechanism causing the peak event, and that this mechanism is potentially different to the mechanism causing the general load fluctuations. The measurements show that about 80% of the peak overall torsion occur simultaneously with the peak overall along wind drag for some generic building shapes. However, the peak torsional response occurs simultaneously with only 30%-40% of the peak overall drag for the rectangular model. These results emphasise the importance of load combinations for building design, which are often neglected in the design of medium sized rigid buildings for which the along-wind drag is dominant. Current design wind loading standards from around the world were evaluated against the results to establish their adequacy for building design incorporating wind-induced torsion effects. Although torsion is frequently neglected, for some structural systems it may become more important.