• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Journal of Microbiology and Biotechnology
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• v.33 no.10
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• pp.1317-1328
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• 2023

Green tea (GT) polyphenols undergo extensive metabolism within gastrointestinal tract (GIT), where their derivatives compounds potentially modulate the gut microbiome. This biotransformation process involves a cascade of exclusive gut microbial enzymes which chemically modify the GT polyphenols influencing both their bioactivity and bioavailability in host. Herein, we examined the in vitro interactions between 37 different human gut microbiota and the GT polyphenols. UHPLC-LTQ-Orbitrap-MS/MS analysis of the culture broth extracts unravel that genera Adlercreutzia, Eggerthella and Lactiplantibacillus plantarum KACC11451 promoted C-ring opening reaction in GT catechins. In addition, L. plantarum also hydrolyzed catechin galloyl esters to produce gallic acid and pyrogallol, and also converted flavonoid glycosides to their aglycone derivatives. Biotransformation of GT polyphenols into derivative compounds enhanced their antioxidant bioactivities in culture broth extracts. Considering the effects of GT polyphenols on specific growth rates of gut bacteria, we noted that GT polyphenols and their derivate compounds inhibited most species in phylum Actinobacteria, Bacteroides, and Firmicutes except genus Lactobacillus. The present study delineates the likely mechanisms involved in the metabolism and bioavailability of GT polyphenols upon exposure to gut microbiota. Further, widening this workflow to understand the metabolism of various other dietary polyphenols can unravel their biotransformation mechanisms and associated functions in human GIT.

• Journal of KIBIM
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• v.13 no.4
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• pp.74-84
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• 2023

Modern construction projects have stakeholders from various construction fields, highlighting the importance of efficient information sharing and collaboration. The expanding scope of Building Information Modeling (BIM), particularly in the domestic construction sector, necessitates a Common Data Environment (CDE). However, applying foreign commercial CDE solutions within the domestic context is challenging due to the difficulty of aligning them with the unique organizational structures and characteristics prevalent in the country. Furthermore, the information review and approval processes specified by ISO 19650 often fail to harmonize adequately with the domestic design procedures, limiting the full utilization of CDE advantages. This study endeavors to develop a Korean CDE collaborative platform based on ISO 19650, with a focus on adapting workflows and information container systems to the domestic context. Building upon the requirements of ISO 19650-based CDE workflows and information containers, this research involves an in-depth analysis of information generation, sharing, review, and approval processes within domestic design organizations, offering tailored CDE workflows and information container systems that align with the specific needs of the Korean construction industry.

• Korean Circulation Journal
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• v.53 no.1
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• pp.1-16
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• 2023

Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.243-255
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• 2024

This study compares Static Terrestrial Laser Scanning (STLS)with the conventional Total Station (TS) method for the geometric assessment of cylindrical storage tanks. With the crucial need for maintaining tank integrity in the oil and gas industry, STLS and TS methods are evaluated for their efficacy in assessing tank deformations. Using STLS and TS, the roundness and verticality of two cylindrical tanks were examined. A deformation analysis based on American Petroleum Institute (API) standards was then provided. Key objectives included comparing the two methods according to API standards, evaluating the workflow for STLS point cloud processing, and presenting the pros and cons of the STLS method for tank geometric assessment. The study found that STLS, with its detailed and high-resolution data acquisition, offers a substantial advantage in having a comprehensive structural assessment over TS. However, STLS requires more processing time and prior knowledge about the data to tune certain parameters and achieve accurate assessment. The project outcomes intend to enhance industry professionals' understanding of applying STLS and TS to tank assessments, helping them choose the best method for their specific requirements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.27-39
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• 2009

This paper describes a case study on an enterprise ontology(30) based analysis and representation of the production operation in shipbuilding. The production operation consists of steel fabrication, assembly, election, launching, sea trial and delivery process. We represent and analyze the steel fabrication process and the piping design business of the assembly process among them. First, we build an ontology on concepts of steel fabrication process and the piping design business of assembly process. And then we merge it with the original EO. We represent each process and analyze current state of production process with the merged EO and $Prot{\acute{e}}g{\acute{e}}$ plug-ins. Moreover, we can analyze dependency relations among the workflow elements. Through the case study, we have found the effectiveness of EO in business management and process management in complex heavy industries.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.939-945
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• 2023

This study analyzes the 'performance efficiency' of AI-based reading assistance systems in the field of radiology by measuring their 'time behavior' properties. Due to the increase in medical images and the limited number of radiologists, the adoption of AI-based solutions is escalating, stimulating a multitude of studies in this area. Contrary to the majority of past research which centered on AI's diagnostic precision, this study underlines the significance of time behavior. Using 50 chest X-ray PA images, the system processed images in an average of 15.24 seconds, demonstrating high consistency and reliability, which is on par with leading global AI platforms, suggesting the potential for significant improvements in radiology workflow efficiency. We expect AI technology to play a large role in the field of radiology and help improve overall healthcare quality and efficiency.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.15-20
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• 2024

In the field of ship inspection in South Korea, due to outdated workflow processes, there is a possibility of tampering with inspection results. Accordingly, research is being conducted to prevent tampering with inspection results by introducing blockchain technology and cloud-based systems that allow real-time tracking and sharing of data, and to establish a transparent and efficient communication system. In this study, unit and integrated processes for overall data management and inspection execution related to ship inspection were implemented to automatically collect, manage, and track various inspection results occurring during the ship inspection process. Through this, it aimed to increase the efficiency of the ship inspection process overall, inducing growth in the ship inspection industry as a whole. The implemented web portal reached a level where trend analysis and comparative analysis with other ships based on inspection results are possible, and subsequent research aims to demonstrate the excellence of the system.

• Journal of Korea Spatial Information System Society
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• v.1 no.1 s.1
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• pp.89-98
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• 1999

The major purpose of this study wa to desgin a external metadata of National Geography Institute(NGI) for the effective management and distribution of the digital maps. For designing a standard external metadata reflecting the current trends of the international organizations on standardization, a prelimenary study was made mainly concentrating on the analysis of the metadata of the developed countries along with domestic cases. For better assessing NGI needs, all the metadata related material of the NGI were collected and classified based on the NGI's workflow. The external metadata draft was made considering the results from the analysis of the existing NGI material and the draft was also cited some major cores from the ISO standards. Continuous efforts should be made in the future to update the metadata draft based on the opinions from the NGI engineers and the technical trends of the international organizations.