• Journal of Information Technology Services
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• v.20 no.4
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• pp.113-128
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• 2021

In the rapidly developing 4th industrial revolution. RPA is increasing in use at home and abroad due to its advantages of simplifying workflow and providing flexibility and scalability at the same time. Thus, this paper conducted an empirical study on companies using RPA to determine which factors affect the intention to use the services provided by RPA systems. As system characteristics, exogenous variables were selected as information quality, system quality, and service quality of the information system success model. The endogenous variables were selected as the system acceptance factors for the performance and effort expectancy of the integrated technology acceptance model, and the perceived economic values and functional values were additionally selected. For the purpose of this study, a structured questionnaire was used for empirical analysis and the proposed hypothesis was verified through the path analysis of structural equations. As a result of the study, there was no significant relationship between service quality and effort expectancy, between service quality and economic value, and it was verified that the relationship between other factors was positively significant.

• 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.

• 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 Microbiology and Biotechnology
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• v.33 no.10
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• pp.1351-1360
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• 2023

Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.2
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• pp.163-188
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• 2018

How do we create a next generation Records Management System? Under a rapidly changing system development environment, the records management system of public institutions has remained stable for the past 10 years. For this reason, it seems to be the key cause of the structural problem of the Records Management System, which makes it difficult to accommodate user requirements and apply a new recording technology. The purpose of this study is to present a redesigned model for a next-generation records management system by analyzing the status of the electronic records management. This study analyzed "A Study on the Redesign of the Next-Generation Electronic Records Management Process," records management technology of advanced records management system, and a case of an overseas system. Based on the analysis results, the improvement direction of the records management system was analyzed from several aspects: functional, software design, and software distribution. This study thus suggests that the creation of a microservice architecture-based (MSA) and an open source software-oriented (OSS) records management system should be the focus of next-generation record management.

• Journal of Korean Society on Water Environment
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• v.40 no.1
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• pp.19-35
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• 2024

The global pandemic, coronavirus disease caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the implementation of wastewater surveillance as a means to monitor the spread of SARS-CoV-2 prevalence in the community. The challenging aspect of establishing wastewater surveillance requires a well-equipped laboratory for wastewater sample analysis. According to previous studies, RT-PCR-based molecular tests are the most widely used and popular detection method worldwide. However, this approach for the detection or quantification of SARS-CoV-2 from wastewater demands a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically takes 6 to 8 hours to provide results for a few samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at regional laboratories. In some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories. The ongoing research and development of alternative and rapid technologies, namely RT-LAMP, ELISA, Biosensors, and GeneXpert, offer a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses. This study aims to discuss the effective regional rapid detection and quantification methods in community wastewater.