• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.347-355
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• 2022

The safety valve (PSV) is a safety device that automatically releases a spring when the pressure generated by various causes reaches the set pressure, and is restored to a normal state when the pressure falls below a certain level. Periodic inspection and monitoring of safety valves are essential so that they can operate normally in abnormal conditions such as pressure rise. However, as the current safety inspection is performed only at a set period, it is difficult to ensure the safety of normal operation. Therefore, evaluation items were developed by finding failure modes and causative factors of safety valves required for safety management. In addition, it is intended to provide decision-making information for securing safety by deriving the priority of items. To this end, a Delphi survey was conducted three times to derive evaluation factors that were judged to be important in relation to the Failure Mode Cause Factor (FMCFs) of the safety valve (PSV) targeting 15 experts. As a result, 6 failure modes of the safety valve and 22 evaluation factors of its sub-factors were selected. In order to analyze the priorities of the evaluation factors selected in this way, the hierarchical structure was schematized, and the hierarchical decision-making method (AHP) was applied to the priority calculation. As a result of the analysis, the failure mode priorities of FMCFs were 'Leakage' (0.226), 'Fail to open' (0.201), 'Fail to relieve req'd capacity' (0.152), 'Open above set pressure' (0.149), 'Spuriously' 'open' (0.146) and 'Stuck open' (0.127) were confirmed in the order. The lower priority of FMCFs is 'PSV component rupture' (0.109), 'Fail to PSV size calculation' (0.068), 'PSV Spring aging' (0.065), 'Erratic opening' (0.059), 'Damage caused by improper installation and handling' (0.058), 'Fail to spring' (0.053), etc. were checked in the order. It is expected that through efficient management of FMCFs that have been prioritized, it will be possible to identify vulnerabilities of safety valves and contribute to improving safety.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.24-48
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• 2022

Bronze mirrors found in pagodas were regarded as simple offerings and thus have received little attention in studies. Furthermore, the few studies on bronze mirrors enshrined in pagodas have focused on the line-engraved mirrors found in the pagodas of the Kingdom of Wuyue; therefore, it is difficult to understand the general characteristics of the bronze mirrors enshrined in the pagodas. This study assumes that the bronze mirrors found in many pagodas in the Kingdom of Wuyue and the Song dynasty were enshrined for a specific purpose. To explore this assumption and accurately understand the artifacts, this study focuses on the location and method of enshrinement. The number of bronze mirrors enshrined in pagodas increased during the Kingdom of Wuyue, with the mirrors expressing statues and inscriptions related to Buddhism rather than being simple offerings. This shows that the purpose of the bronze mirror changed. The influence of the Kingdom of Wuyue continued during the Song dynasty; however, the pattern of bronze mirror enshrinement changed due to the culture and social atmosphere of the time. The most common types of enshrined bronze mirrors were plain, and bronze mirrors from the Dang dynasty were also used consistently. Plain bronze mirrors were used more frequently in this period despite the lingering influence of the Kingdom of Wuyue because it was less laborious to engrave images and inscriptions such as the inscription of Buddha. Additionally, bronze mirrors were valued during this period because of the attention toward the imitation of the archaic bronze(仿古銅器) of the Song dynasty and the influence of the emperor. Moreover, it is believed that bronze mirrors were enshrined in pagodas as offerings as they were deemed valuable at the time. There was a change in the method of enshrining bronze mirrors in pagodas during the Kingdom of Wuyue and the Song dynasty. During this time, bronze mirrors that were positioned on the floor or in iron boxes were intentionally attached to walls or hung from the ceiling. This method was largely divided into two types: attaching to walls or the ceiling(嵌入鏡) and hanging from the ceiling(懸鏡). A typical example of hanging a bronze mirror from the ceiling can be seen in the Jingzhisa Temple Pagoda, and Teng County's Fushengsa Temple Pagoda contains an example of attaching a bronze mirror to the ceiling. The methods of hanging or attaching bronze mirrors to the ceiling were closely related to the methods employed in Chinese tombs. Song dynasty burial chambers had a high and wide structure, so to defend against evil spirits(辟邪用), bronze mirrors were used to protect the burial rooms. Bronze mirrors were, therefore, placed high to illuminate the burial room. This was achieved in the ways mentioned above. As underground chambers became wider and higher, mirrors also protected the important areas of the chambers and illuminated the interiors. Thus, it is believed that the methods of enshrining bronze mirrors in the pagodas during the Kingdom of Wuyue and the Song dynasty arose from the method of enshrining bronze mirrors in tombs at the time. Thereafter, pagodas, such as the Miaojuesa Temple Pagoda, in which the placement of a bronze mirror was actively considered from the design stage were constructed.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1092-1099
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• 2022

Research on exhaust aftertreatment devices to reduce air pollutants and greenhouse gas emissions is being actively conducted. However, in the case of the particulate matters/nitrogen oxides (PM/NOx) simultaneous reduction device for ships, the problem of back pressure on the diesel engine and replacement of the filter carrier is occurring. In this study, for the optimal design of the integrated device that can simultaneously reduce PM/NOx, an appropriate standard was presented by studying the flow inside the device and change in back pressure through the inlet/outlet pressure. Ansys Fluent was used to apply porous media conditions to a diesel particulate filter (DPF) and selective catalytic reduction (SCR) by setting porosity to 30%, 40%, 50%, 60%, and 70%. In addition, the ef ect on back pressure was analyzed by applying the inlet velocity according to the engine load to 7.4 m/s, 10.3 m/s, 13.1 m/s, and 26.2 m/s as boundary conditions. As a result of a computational fluid dynamics analysis, the rate of change for back pressure by changing the inlet velocity was greater than when inlet temperature was changed, and the maximum rate of change was 27.4 mbar. This was evaluated as a suitable device for ships of 1800kW because the back pressure in all boundary conditions did not exceed the classification standard of 68mbar.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.460-467
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• 2022

The purpose of this study is to enhance utilization of the waste nutrient solution (WNS) disposed at the hydroponic greenhouse. Several sets of testing were conducted to examine the effects of WNS: (a) a fertilizer effect, (b) soil column leaching, and (c) crop cultivation. The fertilizer effect test was applied in young radish cultivation by examining the growth characteristics of young radish and soil based on inorganic nitrogen according to the soil treatment of the nitrogen fertilizer (NF) and the WNS. The fertilizer effects and crop cultivation test were conducted with five treatments (A-E): A, non-treatment (water); B, 100% of NF; C, 70% of NF + 30% of WNS; D, 50% of NF + 50% of WNS; and E, 30% of NF + 70% of WNS. The soil column leaching test was conducted with three treatments: non-treatment (water), 100% of NF, 50% of WNS + 50% of NF. As a result, the chemical properties of the WNS were pH 6.0, EC 2.4dS·m^-1, total phosphorus (T-P) 28mg·L^-1, ammonium nitrogen (NH₄-N) 5.0mg·L^-1, and nitrate nitrogen (NO₃-N) 301mg·L^-1. The chemical properties of the soil were pH 5.51, EC 0.31dS/m, organic matter 2.08g·kg^-1, NO₃-N 9.64mg·kg^-1, and NH₄-N 3.20mg·kg^-1. The results of fertilizer effects showed that the ratio of 50% or less of NF and 50% or more of WNS was high in young radish growth. There was no statistically significant difference between the soil chemistry in the C-E treatments where WNS was mixed with NF and the B treatment where only NF was applied. As a result of the soil column leaching test, there was no significant difference in the concentrations of NO₃ and NH₄ in the treatment of 100% of NF and 50% of NF + 50% of WNS. The study indicates, if the mixed fertilizer of WNS and NF is applied in the soil cultivation of young radish, it will reduce the use of NF and environmental pollution. This also helps reduce production costs on farmers and increase the yield of young radish.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.220-256
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• 2022

The most important thing for conserving plant diversity in an area is to make an overall inventory of the plant species inhabiting the area. In particular, limestone areas are known for their high plant diversity and distribution of specific plants. Despite that, only a few have been designated as protected areas. This study investigated the vascular plants distributed in Goyangsan Mtn. and Munraesan Mtn., located in limestone areas of the central part of the Korean Peninsula. A field survey was conducted eight times from April to October 2021. As a result, we identified a total of 654 taxa comprising 113 families, 357 genera, 592 species, 15 subspecies, 44 varieties, and 3 formulas. They included four endangered wild plant species: Astilboides tabularis, Eleutherococcus senticosus, Cypripedium macranthos, and Epilobium hirsutum. Endemic plants in Korea were identified as 32 taxa. Floristic target plants were identified as 168 taxa, specifically 5 taxa of grade V, 41 taxa of grade IV, and 36 taxa of grade III. The red data plants included 2 taxa as "Endangered (EN)", 7 taxa as "Vulnerable (VU)", and 7 taxa as "Near threatened (NT)". A total of 41 taxa of naturalized plants were identified, and 4 of them were invasive alien plants. The surveyed vicinity of Goyangsan Mtn. and Munraesan Mtn. showed high plant diversity and contained core habitats for distribution of an endangered wild plant, Astilboides tabularis,in the limestone area. Moreover, both mountains contained a small population of Cotoneaster integerrimus. These findings confirm that the area has conservation values. Therefore, we propose to identify areas with high plant diversity and designate them as special protected areas.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.