Nuclear power plants are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of nuclear power plants have been constructed, there is currently no specific internationally agreed guidance for their modeling. This paper presents an initiative by the OECD Nuclear Energy Agency called "Digital I&C PSA - Comparative application of DIGital I&C Modelling Approaches for PSA (DIGMAP)", which aimed to advance the field towards practical and defendable modeling principles. The task, carried out in 2017-2021, used a simplified description of a plant focusing on the digital I&C systems important to safety, for which the participating organizations independently developed their own PSA models. Through comparison of the PSA models, sensitivity analyses as well as observations throughout the whole activity, both qualitative and quantitative lessons were learned. These include insights on failure behavior of digital I&C systems, experience from models with different levels of abstraction, benefits from benchmarking as well as major contributors to the core damage frequency and those with minor effect. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key software and common cause failure parameters.
Journal of the Korean Institute of Rural Architecture
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v.26
no.3
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pp.1-9
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2024
This study aims to estimate the cover classification and carbon respiration of halophytes based on the issues of utilising blue carbon in recent context of climate change. To address the aims, the study classified halophytes(Triglochin maritimum L and Phragmites australis), Intertidal(non-vegetated tidal flats) and Supratidal(sandy tidal flats) to measure carbon respiration and classify cover. The results are revealed that first, the carbon respiration in vegetated areas was less than that in non-vegetated areas. Second, the cover classification could be divided into halophyte communities(Triglochin maritimum L, Phragmites australis), Intertidal and Supratidal by NDWI(Moisture Index, Normalized Difference Water Index) Third, the total carbon respiration of blue carbon was calculated to be -0.0121 Ton km2 hr-1 with halophyte communities at -0.0011 Ton km2 hr-1, Intertidal respiration at -0.0113 Ton km2 hr-1 and Supratidal respiration at 0.0003 Ton km2 hr-1. As this challenge is a fundamental study that calculates the quantitative net carbon storage based on the blue carbon-based marine ecosystem, contributing to firstly, measuring the carbon respiration of cordgrass communities, reed communities, and non-vegetated tidal flats, which are potential blue carbon candidates in the study area, to establish representative values for carbon respiration, secondly, verifying the reliability of cover classification of native halophytes extracted through image classification technology, and thirdly, challenging to create a thematic map of carbon respiration, calculating the area and carbon respiration for each classification category.
Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.
Park, Myung-Sook;Jung, Hahn Chul;Lee, Seonju;Ahn, Jae-Hyun;Bae, Sujung;Choi, Jong-Kuk
Korean Journal of Remote Sensing
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v.37
no.5_2
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pp.1281-1293
/
2021
The recent launch of the GOCI-II enables South Korea to have the world's first capability in deriving the ocean color data at geostationary satellite orbit for about 20 years. It is necessary to develop a consistent long-term ocean color time-series spanning GOCI to GOCI-II mission and improve the accuracy through validation using in situ data. To assess the GOCI-II's early mission performance, the objective of this study is to compare the GOCI-II Chlorophyll-a concentration (Chl-a), Colored Dissolved Organic Matter (CDOM), and remote sensing reflectances (Rrs) through comparison with the GOCI data. Overall, the distribution of GOCI-II Chl-a corresponds with that of the GOCI over the Yellow Sea, Korea Strait, and the Ulleung Basin. In particular, a smaller RMSE value (0.07) between GOCI and GOCI-II over the summer Ulleung Basin confirms the GOCI-II data's reliability. However, despite the excellent correlation, the GOCI-II tends to overestimate Chl-a than the GOCI over the Yellow Sea and Korea Strait. The similar over-estimation bias of the GOCI-II is also notable in CDOM. Whereas no significant bias or error is found for Rrs at 490 nm and 550 nm (RMSE~0), the underestimation of Rrs at 443 nm contributes to the overestimation of GOCI-II Chl-a and CDOM over the Yellow Sea and the Korea Strait. Also, we show over-estimation of GOCI-II Rrs at 660 nm relative to GOCI to cause a possible bias in Total suspended sediment. In conclusion, this study confirms the initial reliability of the GOCI-II ocean color products, and upcoming update of GOCI-II radiometric calibration will lessen the inconsistency between GOCI and GOCI-II ocean color products.
With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.
Park, Yeon Soo;Choi, Sun Min;Yang, Won Yeol;Hong, Hye Jin;Kim, Woon Hyung
Journal of Korean Society of Steel Construction
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v.20
no.5
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pp.609-616
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2008
Recently, many ocean bridges that connect land to island or island to island have been constructed along with the improvement of the nation's economy. Long-span bridges can be categorized as suspension bridge, cable-stayed bridge, arch bridge and truss bridge. In this study, correction with respect to construction error can be presented on site through the monitoring of the cable tension change of real structure for four major construction stages so that construction accuracy, including the management of profiles, can be improved. A vibration method, the so-called indirect method that uses the cable's natural frequency changes from the acceleration sensor installed on the cable, is applied in measuring cable tension. In this study, the estimation formula for the effective length of cable with damper is presented by comparing and analyzing between actual measurement and analysis result for the change of the cable's effective length. By the way, it is known that the reliability of estimating cable tension by applying the former method that uses the net distance from damper to anchorage is low. Therefore, for future reference of the maintenance stage, the presented formula for estimating the effective length of cable can be used as a reference for the rational decision-making, such as the re-tensioning and replacement of cable.
The failure behavior of RC structure was exceedingly affected by the size and the local strain distribution of the failure zone due to the strain localization behavior on the tension softening materials. However, it is very difficult to quantify and assess the local strain occurring in the failure zone by the conventional test method. In this study, image processing technology, which is available to measure the strain up to the complete failure of RC structures, was used to estimate the local strain distribution and the size of failure zone. In order to verify the reliability and validity for the image processing technology, the strain transition acquired by the image processing technology was compared with strain values measured by the concrete gauge on the uniaxial compressive specimens. Based on the verification of image processing technology for the uniaxial compressive specimens, the size and the local strain distribution of the failure zone of deep beam was measured using the image processing technology. With the results of test, the principal tensile/compressive strain contours were drawn. Using the strain contours, the size of the failure zone and the local strain distribution on the failure of the deep beam was evaluated. The results of strain contour showed that image processing technology is available to assess the failure behavior of deep beam and obtain the local strain values on the domain of the post-peak failure comparatively.
This study was conducted to investigate the national methane emission from livestock enteric fermentation. For methane emission estimation, livestock were mainly categorized to cattle, swine, poultry, sheep, goats and horses, and cattle were further sub-categorized to calves, fattening cattle, breeding cows in Hanwoo and calves, fattening cattle and lactating cows in dairy cattle. Tier 2 methane emission factors were deduced based on the characteristics of animal performances, live weight, slaughter weight, daily weight gain, and feed digestibility in each category. Tier 2 emission factors of Hanwoo range from 39 to 49 kg/head/year and it is similar to that of Tier 1(47kg/head/year). Tier 2 emission factor of dairy cattle was 107 kg/head/year and it is slightly lower than that of Tier 1(118kg/head/year). Total methane emission from livestock enteric fermentation by Tier 2 method was estimated to be 126.8 tones in 2001. The methane emissions by Hanwoo, dairy cattle, swine, goats, horses and sheep were 61.70, 47.76, 13.08, 2.25, 0.17 and 0.01 tones, respectively. By the use of Tier 2 method instead of Tier 1, the accuracy and reliability of methane emission estimates from livestock enteric fermentation in Korea is considered to be improved.
KSCE Journal of Civil and Environmental Engineering Research
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v.32
no.4D
/
pp.345-356
/
2012
Road pavement requires repetitive maintenance works to maintain satisfactory service level to the public. However, the repetitive maintenance works upon deteriorated pavement structure make negative effects to deterioration speed. It often leads to inefficient use of limited budget. For that reason, the pavements require reconstruction work to recover their original performance. Recently, construction demands in the Korean national highway have already been reached to maximum level, and the aged pavements start to demand much more reconstruction works. However, in the real world, road agencies have often been confused when they determine maintenance design for such aged road sections due to budget constraint. It is because there is no reliable long-term maintenance strategy that supports their decision making. To support their decision making, this paper aimed to suggest the best maintenance strategy considering changing process of pavement performance by repetitive maintenance works. As an analysis method, probability distribution and hazard function to estimate the life expectancy were adopted, and then the results were used for long-term life cycle cost analysis with deterministic or Monte-Carlo method under various scenarios. As an empirical study, the Korean national highway data that has long-maintenance history data since 1986 has been applied. Last, this paper considered quality assurance of maintenance work to improve maintenance quality. These could be important information as a part of long-term maintenance strategy of pavement.
The majority of daily travel demands concentrate at particular time-periods, which causes the difficulties in the travel demand analysis and the corresponding benefit estimation. Thus, it is necessary to consider time-specific traffic characteristics to yield more reliable results. Traditionally, na$\ddot{i}$ve, heuristic, and statistical approaches have been applied to address the peak-hour ratio. In this study, a hybrid clustering model which is one of the statistical methods is applied to calculate the peak-hour ratio and its duration. The 2009 national 24-hour traffic data provided by the Korea institute of Construction Technology are used. The analysis is conducted dividing vehicle types into passenger cars and trucks. For the verification for the usefulness of the methodology, the toll collection system data by the Korea Express Corporation are collected. The result of the research shows lower errors during the off-peak hours and night times and increasing error ratios as the travel distance increases. Since the method proposed can reduce the arbitrariness of analysts and can accommodate the statistical significance test, the model could be considered as a more robust and stable methodology. It is hoped that the result of this paper could contribute to the enhancement of the reliability for the travel demand analysis.
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