• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Molecules and Cells
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• v.26 no.2
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• pp.206-211
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• 2008

HI0381 of Haemophilus influenzae was investigated by circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. HI0381 is a 153-residue peptidoglycan-associated outer membrane lipoprotein, and a part of the larger Tol/Pal network. Here, we report its backbone $^1H$, $^{15}N$, and $^{13}C$ resonance assignments, and secondary structure predictions. About 97% of all of the $^1HN$, $^{15}N$, $^{13}CO$, $^{13}C{\alpha}$, and $^{13}C{\beta}$ resonances covering 131 non-proline residues of the 134 residue, mature protein, were clarified by sequential and specific assignments. CSI and TALOS analyses revealed that HI0381 contains five ${\alpha}$-helices and five ${\beta}$-strands. To characterize the structure of HI0381, the effects of pH and salt concentration were investigated by CD. In addition, the structural changes occurring when HI0381 was in a membranous environment were investigated by comparing its HSQC spectra and CD data in buffer and in DPC micelles; the results showed that helix ${\alpha}4$ and strand ${\beta}4$ became aligned with the membrane. We conclude that the conformation of HI0381 is affected by the membrane environment, implying that its folded state is directly related to its function.

• Bulletin of the Korean Chemical Society
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• v.33 no.10
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• pp.3248-3252
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• 2012

Vibrio extracellular metalloprotease (vEP), secreted from Vibrio vulnificus, shows various proteolytic function such as prothrombin activation and fibrinolytic activities. Premature form of vEP has an N-terminal (nPP) and a C-terminal (C-ter100) region. The nPP and C-ter100 regions are autocleaved for the matured metalloprotease activity. It has been proposed that two regions play a key role in regulating enzymatic activity of vEP. Especially, C-ter100 has a regulatory function on proteolytic activity of vEP. C-ter100 domain has been cloned into the E. coli expression vectors, pET32a and pGEX 4T-1 with TEV protease cleavage site and purified using gel-filtration chromatography followed by affinity chromatography. To understand how C-ter100 modulates proteolytic activity of vEP, structural studies were performed by heteronuclar multi-dimensional NMR spectroscopy. Backbone $^1H$, $^{15}N$ and $^{13}C$ resonances were assigned by data from standard triple resonance and HCCH-TOCSY experiments. The secondary structures of vEP C-ter100 were determined by TALOS+ and CSI software based on hydrogen/deuterium exchange. NMR data show that C-ter100 of vEP forms a ${\beta}$-barrel structure consisting of eight ${\beta}$-strands.

• The korean journal of orthodontics
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• v.19 no.1 s.27
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• pp.7-20
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• 1989

A serial cephalometric study was undertaken to define the growth of the soft tissue facial profile in Korean children. The sample was composed of 25 males and 15 females for whom yearly cephalometric records were taken from the ages of 6 to 13 years. From the tracings, points on skeletal and soft tissue profiles were located and recorded on magnetic tape utilizing a Calcomp Talos RP660 X-Y digitizer. Linear and angular measurements of soft tissues were made directly from tape in a Cyber 174-16 computer after cephalometric enlargement had been corrected. A statistical evaluation was made of the data and the average profile diagrams in male and female were described by a Calcomp 960 pen plotter. On the basis of the findings of this study, the following trends were established. 1. The most prominent growth in soft tissue facial profile thickness was the nose and the least was the forehead. 2. The general growth direction of the soft facial tissue to the cranium described the downward and forward. 3. The degree of soft tissue facial convexity was decidely more than that exhibited earlier in life even though the soft tissue chin had protruded to the cranium. 4. The measurements indicated a general tendency for males to have larger nose and more convex and long soft tissue facial profile than did females. 5. Males showed significantly more growth than females in base of the upper lip and height of the upper anterior facial profile. 6. There was a difference between males and females in the rates of soft tissue facial profile growth. 7. Korean children showed less convex in the soft tissue profile convexity than did American children.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.