• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.2
• /
• pp.97-109
• /
• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.79-89
• /
• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Nuclear Engineering and Technology
• /
• v.54 no.1
• /
• pp.84-96
• /
• 2022

In the worst case, a temporary ignition source (also known as transient combustibles) between two electrical panels can damage both panels. Mitigation strategies for electrical panel fires were previously developed using fire modeling and risk analysis. However, since they do not comply with deterministic fire protection requirements, it is necessary to analyze the boundary values at which combustibles may damage targets depending on various factors. In the present study, a sensitivity analysis of input variables related to the damage threshold of two electrical panels was performed for dimensionless geometry using a Fire Dynamics Simulator (FDS). A new methodology using a damage evaluation map was developed to assess the damage of the electrical panel. The input variables were the distance between the electrical panels, the vertical height of the fuel, the size of the fire, the wind speed and the wind direction. The heat flux was determined to increase as the vertical distance between the fuel and the panel decreased, and the largest heat flux was predicted when the vertical separation distance divided by one half flame length was 0.3-0.5. As the distance between the panels increases, the heat flux decreases according to the power law, and damage can be avoided when the distance between the fuel and the panel is twice the length of the panel. When the wind direction is east and south, to avoid damage to the electrical panel the distance must be increased by 1.5 times compared to no wind. The present scale model can be applied to any configuration where combustibles are located between two electrical panels, and can provide useful guidance for the design of redundant electrical panels.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.4195-4208
• /
• 2022

In this study, operator habitability was numerically evaluated in the event of a fire at the main control bench board (MCB) in a reference main control room (MCR). It was investigated if evacuation variables including hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD) at 1.8 m from the MCR floor exceed the reference evacuation criteria provided in NUREG/CR-6850. For a fire model validation, the simulation results of the reference MCR were compared with existing experimental results on the same reference MCR. In the simulation, various input parameters were applied to the MCB panel fire scenario: MCR height, peak heat release rate (HRR) of a panel, number of panels where fire propagation occurs, fire propagation time, door open/close conditions, and mechanical ventilation operation. A specialized-average HRR (SAHRR) concept was newly devised to comprehensively investigate how the various input parameters affect the operator's habitability. Peak values of the evacuation variables normalized by evacuation criteria of NUREG/CR-6850 were well-correlated as the power function of the SAHRR for the various input parameters. In addition, the evacuation time map was newly utilized to investigate how the evacuation time for different SAHRR was affected by changing the various input parameters. In the previous studies, it was found that the OD is the most dominant variable to determine the MCR evacuation time. In this study, however, the evacuation time map showed that the HF is the most dominant factor at the condition of without-mechanical ventilation for the MCR with a partially-open false ceiling, but the OD is the most dominant factor for all the other conditions. Therefore, the method using the SAHRR and the evacuation time map was very useful to effectively and comprehensively evaluate the operator habitability for the various input parameters in the event of MCB fires for the reference MCR.

• Genomics & Informatics
• /
• v.20 no.4
• /
• pp.45.1-45.7
• /
• 2022

Food security will be affected by climate change worldwide, particularly in the developing world, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and food quality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes in plants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promoters of five auxin-responsive genes in Zea mays. We applied a data fusion approach based on the combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a direct impact on cell membrane proteins. The short-term auxin response may be represented by the regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this gene under auxin treatment for 30 min indicated the role of POP in maize auxin response. POP is regulated by auxin to perform stress adaptation. In addition, the detection of two AuxRE TGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4B
• /
• pp.433-437
• /
• 2006

Recently, mathematical modeling for the activated sludge process is important to design and control of wastewater treatment plant. Nevertheless, there is a lack of information regarding the pathway of substrate utilization between external and internal substrates in biological nutrient removal (BNR). In this research, a new activated sludge model (step growth model) is proposed and compare with ASM No.3. This model structure is consist of five processes; aerobic storage, growth on external substrate and stored intercellular storage compounds (ISCs), endogenous respiration and aerobic respiration of ISCs. The predicted results by the step growth model were more good accordance with the results of oxygen utilization rate (OUR) and TCOD experiment than that of the ASM No.3.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.149-156
• /
• 2020

Blast furnace revamping in steel industry is one of the most important work to complete the complicated equipment within a short period of time based on the interfaces of various types of work. P company has planned to build a Smart Tracking System based on the wireless tag system with the aim of complying with the construction period and reducing costs, ahead of the revamping of blast furnace scheduled for construction in February next year. It combines the detailed design data with the wireless recognition technology to grasp the stage status of design, storage and installation. Then, it graphically displays the location information of each member in relation to the plan and the actual status in connection with Building Information Modeling (BIM) 4D Simulation. QR Code is used as a wireless tag in order to check the receiving status of core equipment considering the characteristics of each item. Then, DB in server system is built, status information is input. By implementing BIM 4D Simulation data using DELMIA, the information on location and status is provided. As a feature of the S/W function, a function for confirming the items will be added to the cellular phone screen in order to improve the accuracy of tagging of the items. Accuracy also increases by simultaneous processing of storage and location tagging. The most significant effect of building this system is to minimize errors in construction by preventing erroneous operation of members. This system will be very useful for overall project management because the information about the position and progress of each critical item can be visualized in real time. It could be eventually lead to cost reduction of project management.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.81-81
• /
• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• The Journal of Economics, Marketing and Management
• /
• v.12 no.2
• /
• pp.27-36
• /
• 2024

Purpose: As the number of vegetarians continues to rise in tandem with the development of consumer culture, a novel economic trend named 'Vegenomics' has surfaced. In addition, as interest in social and environmental sustainability such as health, environment, and animal welfare grows due to the COVID-19 pandemic, the alternative protein food market is expanding, focusing on plant-based alternative meat. Research design, data, and methodology: Therefore, this study aims to investigate the impact of the consumption value of alternative protein products on self-efficacy and purchase intention. This study collected a total of 187 questionnaires by conducting an online survey from May 1 to July 10, 2023, to verify the research model and hypothesis. The collected data were subjected to exploratory factor analysis, confirmatory factor analysis, and discriminant validity analysis using SPSS 20.0 and AMOS 20.0 programs for structural equation modeling. Results: The results of analyzing consumers' self-efficacy and purchase intention regarding the functional value, health-oriented value, ethical value, and ecological value of alternative protein products are as follows. First, among the consumption values of alternative protein products, ecological value was found to have a significant positive (+) effect on self-efficacy. Second, consumers' self-efficacy for alternative protein products was found to have a significant positive (+) effect on purchase intention. Conclusion: This study is anticipated to provide valuable insights for the formulation of effective marketing strategies for alternative protein products and the development of products that align with consumer needs.

• Animal Bioscience
• /
• v.37 no.7
• /
• pp.1196-1203
• /
• 2024

Objective: A study was conducted to quantify the performance differences of the near-infrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. Methods: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. Results: The performance of the developed NIRS calibration models was evaluated based on R², standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R² (coefficient of determination), standard error, and RPD. Although the R² of calibration models was mainly greater than 0.90 across the feed value indexes, the R² of cross-validations was much lower. The R² of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Conclusion: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.