• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.265-288
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• 2023

Process mining has generally analyzed the default logs of Information Systems such as SAP ERP, but as the use of automation software called RPA expands, the logs by RPA bots can be utilized. In this study, the actual status of RPA automation in the field was identified by applying RPA bots to the work of three domestic manufacturing companies (cosmetic field) and analyzing them after leaving logs. Using Uipath and Python, we implemented RPA bots and wrote logs. We used Disco, a software dedicated to process mining to analyze the bot logs. As a result of log analysis in two aspects of bot utilization and performance through process mining, improvement requirements were found. In particular, we found that there was a point of improvement in all cases in that the utilization of the bot and errors or exceptions were found in many cases of process. Our approach is very scientific and empirical in that it analyzes the automation status and performance of bots using data rather than existing qualitative methods such as surveys or interviews. Furthermore, our study will be a meaningful basic step for bot behavior optimization, and can be seen as the foundation for ultimately performing process management.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.12-21
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• 2023

Green remodeling proposed in the Korean New Deal is a project to build or remodel eco-friendly and energy-efficient buildings using renewable energy facilities and high-performance insulation for public buildings. The government intends to achieve the carbon emission reduction target by conducting green remodeling. Major overseas cities that conduct green remodeling are actively promoting technology support and promotion along with energy performance evaluation according to building characteristics, subsidies for private revitalization, and tax benefits. With this background, the analysis of the current status and problems of the green remodeling project was performed and the Activation factors of Green Remodeling were derived from survey results. This study suggested strategic measures such as a participation of civil society, promotion, and priority selection of administration and policy measures such as a leading role of the public sector, expanding support for the socially underprivileged, and financial support and tax benefits. And this study results are expected to be utilized as basic data to promote the green remodeling project.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.251-260
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• 2023

The construction of free-form structures with intricate curved exteriors necessitates the use of bespoke molds. To fulfill this requirement, a blend of Phase Change Material(PCM) and Ultra-High Performance Concrete(UHPC) is utilized. PCM endows the solution with recyclability, while UHPC facilitates the effortless execution of curvature in the mold fabrication process. However, it's worth mentioning that the melting point of PCM hovers around 58-64℃, and the heat emanating from UHPC's hydration process can potentially jeopardize the integrity of the PCM mold. Hence, experimental validation of the mold shape is a prerequisite. In the conducted experiment, UHPC was poured into two distinct mold types: one that incorporated a 3mm silicone sheet mounted on the fabricated PCM mold(Panel A), and the other devoid of the silicone sheet(Panel B). The experimental outcomes revealed that Panel A possessed a thickness of 3.793mm, while Panel B exhibited a thickness of 5.72mm. This suggests that the mold lacking the silicone sheet(Panel B) was more susceptible to the thermal effects of hydration. These investigations furnish invaluable fundamental data for the manufacturing of ultra-high strength irregular panels and PCM molds. They contribute substantially to the enrichment of comprehension and application of these materials within the realm of construction.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.697-704
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• 2021

It is hard to get Filaments which are materials of the 3D printing Fused Deposition Modeling(FDM) method as radiation shielding in Korea. and also related research is insufficient. This study aims to provide basic data for the development of radiation shields using 3D printing by evaluating the physical properties and radiation shielding capabilities of filaments containing metal particles. after selecting five metal filaments containing metal particle reinforcement materials, the radiation shielding rate was calculated according to the Korean Industrial Standard's protective equipment test method to evaluate physical properties such as tensile strength, density, X-ray Diffraction(XRD), and weight measurement using ASTM's evaluation method. In the tensile strength evaluation, PLA + SS was the highest, ABS + W was the lowest, and ABS + W is 3.13 g/cm³ which value was the highest among the composite filaments in the density evaluation. As a result of the XRD, it may be confirmed that the XRD peak pattern of the particles on the surface of the specimen coincides with the pattern of each particle reinforcing material powder metal, and thus it was confirmed that the printed specimen contained powder metal. The shielding effect for each 3D printed composite filament was found to have a high shielding rate in proportion to the effective atomic number and density in the order of ABS + W, ABS + Bi, PLA+SS, PLA + Cu, and PLA + Al. In this study, it was confirmed that the metal particle composite filament containing metal powder as a reinforcing material has radiation shielding ability, and the possibility of using a radiation shielding filament in the future.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.62-76
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• 2023

Recently, machine learning (ML) techniques have been actively applied for seismic trace interpolation. However, because most research is based on training-inference strategies that treat missing trace gather data as a 2D image with a blank area, a sufficient number of fully sampled data are required for training. This study proposes trace interpolation using ML, which uses only irregularly sampled field data, both in training and inference, by modifying the training-inference strategies of trace-based interpolation techniques. In this study, we describe a method for constructing networks that vary depending on the maximum number of consecutive gaps in seismic field data and the training method. To verify the applicability of the proposed method to field data, we applied our method to time-migrated seismic data acquired from the Vincent oilfield in the Exmouth Sub-basin area of Western Australia and compared the results with those of the conventional trace interpolation method. Both methods showed high interpolation performance, as confirmed by quantitative indicators, and the interpolation performance was uniformly good at all frequencies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.119-125
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• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.

• Tunnel and Underground Space
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• v.33 no.3
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• pp.109-125
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• 2023

Disposal repository for high-level radioactive waste secures its safety by means of engineered and natural barriers. The performance of these barriers should be tested and verified through various aspects in terms of short and/or long-term. KAERI has been conducting various in-situ demonstrations in KURT (KAERI Underground Research Tunnel). After completing previous experiment, a conceptual design of an improved in-situ experiment, i.e. K-COIN (KURT experiment of THMC COupled and INteraction), was established and detailed planning for the experiment is underway. Preliminary characterizations were conducted in KURT for siting a K-COIN test site. 15 boreholes with a depth of about 20 m were drilled in three research galleries in KURT and intact rock specimens were prepared for laboratory tests. Using the specimens, physical measurements, uniaxial compression, indirect tension, and triaxial compression tests were conducted. As a result, specific gravity, porosity, elastic wave velocities, uniaxial compressive strength, Young's modulus, Poisson's ratio, Brazilian tensile strength, cohesion, and internal friction angle were estimated. Statistical analyses revealed that there did not exist meaningful differences in intact rock properties according to the drilled sites and the depth. Judging from the uniaxial compressive strength, which is one of the most important properties, all the specimens were classified as very strong rock so that mechanical safety was secured in all the regions.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.461-470
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• 2023

The purpose of this research is to derive the optimal temperature, location, and heating control system for a tipping bucket rain gauge heating system used for observing snowfall during winter. We conducted indoor and outdoor experiments by manufacturing a tipping bucket rain gauge that can be variably controlled for heating at the funnel, exterior, and interior, and indoor and outdoor. The indoor experiments involved using a temperature and humidity chamber to compare the performance and derive the appropriate temperature of the precipitation gauge heating system. Subsequently, the outdoor experiments were carried out at the Cloud Physics Observation Center located in Daeguallyeong, heavy snowfall region, to validate the findings. The analysis result was derived that the heating temperature of the funnel should be set at the 10 to 30℃, while the internal heating temperature should be 70℃. Furthermore, the optimal locations for the heating devices, which aim to minimize measurement delay, were identified as the exterior of the rain gauge, the rim of the funnel, and the vertical surface of the funnel. Our result shows that used as the basis for the operating conditions of precipitation gauge heating systems for solid precipitation measurement in winter.