• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.29-37
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• 2019

Purpose DMIDR(Discovery Molecular Imaging Digital Ready, General Electric Healthcare, USA) is a PET/CT scanner designed to allow application of PSF(Point Spread Function), TOF(Time of Flight) and Q.Clear algorithm. Especially, Q.Clear is a reconstruction algorithm which can overcome the limitation of OSEM(Ordered Subset Expectation Maximization) and reduce the image noise based on voxel unit. The aim of this paper is to evaluate the performance of reconstruction algorithms and optimize the algorithm combination to improve the accurate SUV(Standardized Uptake Value) measurement and lesion detectability. Materials and Methods PET phantom was filled with $^{18}F-FDG$ radioactivity concentration ratio of hot to background was in a ratio of 2:1, 4:1 and 8:1. Scan was performed using the NEMA protocols. Scan data was reconstructed using combination of (1)VPFX(VUE point FX(TOF)), (2)VPHD-S(VUE Point HD+PSF), (3)VPFX-S (TOF+PSF), (4)QCHD-S-400((VUE Point HD+Q.Clear(${\beta}-strength$ 400)+PSF), (5)QCFX-S-400(TOF +Q.Clear(${\beta}-strength$ 400)+PSF), (6)QCHD-S-50(VUE Point HD+Q.Clear(${\beta}-strength$ 50)+PSF) and (7)QCFX-S-50(TOF+Q.Clear(${\beta}-strength$ 50)+PSF). CR(Contrast Recovery) and BV(Background Variability) were compared. Also, SNR(Signal to Noise Ratio) and RC(Recovery Coefficient) of counts and SUV were compared respectively. Results VPFX-S showed the highest CR value in sphere size of 10 and 13 mm, and QCFX-S-50 showed the highest value in spheres greater than 17 mm. In comparison of BV and SNR, QCFX-S-400 and QCHD-S-400 showed good results. The results of SUV measurement were proportional to the H/B ratio. RC for SUV is in inverse proportion to the H/B ratio and QCFX-S-50 showed highest value. In addition, reconstruction algorithm of Q.Clear using 400 of ${\beta}-strength$ showed lower value. Conclusion When higher ${\beta}-strength$ was applied Q.Clear showed better image quality by reducing the noise. On the contrary, lower ${\beta}-strength$ was applied Q.Clear showed that sharpness increase and PVE(Partial Volume Effect) decrease, so it is possible to measure SUV based on high RC comparing to conventional reconstruction conditions. An appropriate choice of these reconstruction algorithm can improve the accuracy and lesion detectability. In this reason, it is necessary to optimize the algorithm parameter according to the purpose.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.65-73
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• 2019

The MODIS (Moderate Resolution Imaging Spectroradiometer) data in Hierarchical Data Format (HDF) have been processed using the Geospatial Data Abstraction Library (GDAL). Because of a relatively large data size, it would be preferable to build and install the data analysis tool with greater computing performance, which would differ by operating system and the form of distribution, e.g., source code or binary package. The objective of this study was to examine the performance of the GDAL for processing the HDF files, which would guide construction of a computer system for remote sensing data analysis. The differences in execution time were compared between environments under which the GDAL was installed. The wall clock time was measured after extracting data for each variable in the MODIS data file using a tool built lining against GDAL under a combination of operating systems (Ubuntu and openSUSE), compilers (GNU and Intel), and distribution forms. The MOD07 product, which contains atmosphere data, were processed for eight 2-D variables and two 3-D variables. The GDAL compiled with Intel compiler under Ubuntu had the shortest computation time. For openSUSE, the GDAL compiled using GNU and intel compilers had greater performance for 2-D and 3-D variables, respectively. It was found that the wall clock time was considerably long for the GDAL complied with "--with-hdf4=no" configuration option or RPM package manager under openSUSE. These results indicated that the choice of the environments under which the GDAL is installed, e.g., operation system or compiler, would have a considerable impact on the performance of a system for processing remote sensing data. Application of parallel computing approaches would improve the performance of the data processing for the HDF files, which merits further evaluation of these computational methods.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.127-133
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• 2019

Purpose: The purpose of present study was to investigate fracture strength and mode of failure of endodontically treated teeth restored with metal cast post-core system, prefabricated fiber post system, and newly introduced polyetherketoneketone (PEKK) post-core system. Materials and methods: A total of 21 mandibular premolar were randomly grouped into 3 groups of 7 each according to the post material. Group A was for metal cast post core; Group B for prefabricated glass fiber post and resin core; and Group C for milled PEKK post cores. All specimens were restored with metal crown. The fracture strength of each specimen was measured by applying a static load of 135-degree to the tooth at 2 mm/min crosshead speed using a universal testing machine. After the fracture strength measurement, the mode of failure was observed. The results were analyzed using Kruscal-Wallis test and post hoc Mann-Whitney U test at confidence interval ${\alpha}=.05$. Results: Fracture resistance of PEKK post core was lower than those of cast metal post and fiber reinforced post with composite resin core. In the aspect of fracture mode most of the root fracture occurred in the metal post core, whereas the post detachment occurred mainly in the fiber reinforced post. In the case of PEKK post core, teeth and post were fractured together. Conclusion: It is necessary to select appropriate materials of post for extensively damaged teeth restoration and clinical application of the PEKK post seems to require more research on improvement of strength.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.492-506
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• 2019

Heavy metal contaminants in the shooting range are mostly present in a warhead circle or a metal fragment present as a particle, these fine metal particles are weathered for a long period of time is very likely that the surface is present as an oxide or carbon oxide. In particular, lead which is a representative contaminant in the shooting range soil, is present as more fine particles because it increases the softness and is stretched well. Therefore, by physical washing experiment, we conducted a degree analysis, concentration of heavy metals by cubic diameter, composition analysis of metallic substances, and assessment of applicability of gravity, magnetism and floating selection. The experimental results FESEM analysis and the measurement results lead to the micro-balance was confirmed thatthe weight goes outless than the soil ofthe same size in a thinly sliced and side-shaped structure according to the dull characteristics it was confirmed that the high specific gravity applicability. In addition, the remediation efficiency evaluation results using a hydrocyclone applied to this showed a cumulative remediation efficiency of 71%,twice 80%, 3 times 91%. On the other hand, magnetic sifting showed a low efficiency of 17%,floating selection -35mesh (0.5mm)target soil showed a relatively high efficiency to 39% -10mesh (2mm) efficiency was only 16%. The target treatment diameter of soil washing should be 2mm to 0.075mm, which is applied to the actual equipment by adding an additional input classification, which would require management as additional installation costs and processes are constructed. As a result, it is found that the soilremediation of shooting range can be separately according to the size of the warhead. The size is larger than the gravel diameter to most 5.56mm, so it is possible to select a specific gravity using a high gravity. However, the contaminants present in the metal fragments were found to be processed by separating using a hydrocyclone of the soil washing according to the weight is less than the soil of the same particle size in a thinly fragmented structure.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.21-33
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• 2021

The purpose of this study is to determine the order of priority for the use of amendments, matching the optimal amendment to the specific site in Korea. This decision-making process must prioritize the stabilization and economic efficiency of amendment for heavy metals and metalloid based on domestic site contamination scenarios. For this study, total 5 domestic heavy metal contaminated sites were selected based on different pollution scenarios and 13 amendments, which were previously studied as the soil stabilizer. Batch extraction experiments were performed to quantify the stabilization efficiency for 8 heavy metals (including As and Hg) for 5 soil samples, representing 5 different pollution scenarios. For each amendment, the analyses using XRD and XRF to identify their properties, the toxicity characteristics leaching procedure (TCLP) test, and the synthetic precipitation leaching procedure (SPLP) test were also conducted to evaluate the leaching safety in applied site. From results of batch experiments, the amendments showing > 20% extraction lowering efficiency for each heavy metal (metalloid) was selected and the top 5 ranked amendments were determined at different amount of amendment and on different extraction time conditions. For each amendment, the total number of times ranked in the top 5 was counted, prioritizing the feasible amendment for specific domestic contaminated sites in Korea. Mine drainage treatment sludge, iron oxide, calcium oxide, calcium hydroxide, calcite, iron sulfide, biochar showed high extraction decreasing efficiency for heavy metals in descending order. When the economic efficiency for these amendments was analyzed, mine drainage treatment sludge, limestone, steel making slag, calcium oxide, calcium hydroxide were determined as the priority amendment for the Korean field application in descending order.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.46 no.4
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• pp.319-327
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• 2020

Particulate matters (PM) are small particulate pollution that decrease the function of skin barrier, which causes inflammatory skin diseases and extrinsic aging. In this study, we evaluated the effect of preventing the adherence of PMs from several cosmetic products applied to human skin using iron oxide black. The PM floating chamber consists of skin exposure area, PM inlet, floating power device, and an outlet so that PM can be naturally attached to the skin while floating in the chamber. The change in skin brightness according to the floating concentration of alternative fine dust was checked to confirm the optimal floating concentration conditions. The intensity difference (before-after intensity, Δ) before and after adhesion of iron oxide black was proportional to the amount of PM adhered. The anti-adherence effect of iron oxide black on five cosmetic products were evaluated through 20 each subjects by comparing the amount of iron oxide black adhered on the control and treatment. The difference in brightness before and after the iron oxide black attached to the skin was calculated and compared with the control group(p < 0.05). When over 150 mg of iron oxide black was adhered on the skin, the interference of intensity was low and clearly showed the skin adhered pattern. According to the application of the five cosmetics, the intensity difference was significantly lower than the control group. This means that depending on the product, it prevented the attachment of iron oxide black. This study is a safe and useful method to confirm the prevention of PM skin adherence. In conclusion, cosmetics can prevent the adherence of PM on the skin according to the formulation or ingredients characteristics.

• Pediatric Infection and Vaccine
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• v.28 no.2
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• pp.92-100
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• 2021

Purpose: Rapid detection of etiologic organisms is crucial for initiating appropriate therapy in patients with central nervous system (CNS) infection. This study aimed to evaluate the diagnostic value of the BioFire^® Meningitis/Encephalitis (ME) panel in detecting etiologic organisms in cerebrospinal fluid (CSF) samples from febrile infants. Methods: CSF samples from infants aged <90 days who were evaluated for fever were collected between January 2016 and July 2019 at the Seoul National University Children's Hospital. We performed BioFire^® ME panel testing of CSF samples that had been used for CSF analysis and conventional tests (bacterial culture, Xpert^® enterovirus assay, and herpes simplex virus-1 and -2 polymerase chain reaction) and stored at -70℃ until further use. Results: In total, 72 (24 pathogen-identified and 48 pathogen-unidentified) CSF samples were included. Using BioFire^® ME panel testing, 41 (85.4%) of the 48 pathogen-unidentified CSF samples yielded negative results and 22 (91.7%) of the 24 pathogen-identified CSF samples yielded the same results (enterovirus in 19, Streptococcus agalactiae in 2, and Streptococcus pneumoniae in 1) as those obtained using the conventional tests, thereby resulting in an overall agreement of 87.5% (63/72). Six of the 7 pathogen-unidentified samples were positive for human parechovirus (HPeV) via BioFire^® ME panel testing. Conclusions: Compared with the currently available etiologic tests for CNS infection, BioFire^® ME panel testing demonstrated a high agreement score for pathogen-identified samples and enabled HPeV detection in young infants. The clinical utility and cost-effectiveness of BioFire^® ME panel testing in children must be evaluated for its wider application.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.