• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Steel and Composite Structures
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• v.38 no.5
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• pp.547-562
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• 2021

This paper aims to explore the mechanical behavior and moment-rotation model of blind bolted joints between concrete-filled double skin steel tubular columns and steel-concrete composite beams. For this type of joint, the inner tube and sandwiched concrete were additionally identified as basic components compared with CFST blind bolted joint. A modified moment-rotation model for this type of connection was developed, of which the compatibility condition and mechanical equilibrium were employed to determine the internal forces of basic components and neutral axis. Following this, load transfer mechanism among the inner tube, sandwiched concrete and outer tube was discussed to assert the action area of the components. Subsequently, assembly processes of basic coefficients in terms of their stiffness and resistances based on the component method by simplifying them as assemblages of springs in series or in parallel. Finally, an experimental investigation on four substructure joints with CFDST columns for validation purposes was carried out to capture the connection details. The predicted results derived from the mechanical models coincided well with the experimental results. It is demonstrated that the proposed mechanical model is capable of evaluating the complete moment-rotation relationships of blind bolted CFDST column composite connections.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Coupled systems mechanics
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• v.11 no.2
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• pp.107-119
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• 2022

The aim of the work presented in this paper is development of numerical model for prediction of temperature distribution in pavement according to the measured meteorological parameters, with introduction of non-linear heat transfer coefficient which is a function of temerature difference between the air and the pavement. Developed model calculates heat radiated from the pavement back in the air, which is an important part of the heat trasfer process in the open air surfaces. Temperature of the pavement surface, heat radiation together with many meteorological parameters were measured in series during two years in order to validate the model and calibrate model parameters. Special finite element method for temperature heat transfer towards the soil together with the time integration scheme are used to solve the governing equation. It is proved that non-linear heat transfer coefficient, which is a function of time and temperature difference between the air and the pavement, is required to decribe this phenomena. Proposed model includes heat tranfer coefficient callibration for specific climate region, through the iterative inverse procedure.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.111-123
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• 2020

The core competencies for the 21st century are creativity, critical thinking, collaboration, and communication. In recent classes where ICT (information, communication, and technology) is grafted, a lot of efforts are also being made to increase such competencies. According to a research work, ICT is most often used as a communication channel between teachers and students or as an online collaboration tool among students. However, ICT has only played a role as a guideline for instruction, but not included in the curriculum until now. The research on methods how to integrate technology into teaching and learning is in full swing due to the development of technology and the advent of Covid-19. In this paper, we propose a technology integration hierarchy model, namely K-TIHM that can be combined with STEAM education. Since only learning environments have been proposed in the existing research for technology-based STEAM education, our model proposes a series of technology integration hierarchy that can be applied by school age along with STEAM. Also, we analyze the differences in among the Korea's ICT education operation guidelines, the Korea's Software education guidelines, and ours. The proposed model can help developing the primary and secondary school curriculum integrated with technology.

• Journal of the Korean Geotechnical Society
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• v.23 no.5
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• pp.101-110
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• 2007

In order to investigate the effect of the pile shape on the bearing capacity of non-displacement piles, a series of model pile load tests were performed using a calibration chamber and three model piles with different shape. Results of the model tests showed that the bearing capacity of tapered piles was affected by its taper angle as well as the stress states and relative density of soil. Based on the results of model pile load tests, a new design equation for estimation of the bearing capacity of non-displacement piles was proposed, and it takes into account the effect of the taper angles on the bearing capacity of non-displacement piles.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.163-178
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• 2022

This paper aimed to study the local bond-slip behavior between ultra-high-performance concrete (UHPC) and a reinforcing bar after exposure to high temperatures. A series of pull-out tests were carried out on cubic specimens of size 150×150×150 mm with deformed steel bar embedded for a fixed length of three times the diameter of the tested deformed bar. The experimental results of the bond stress-slip relationship were compared with the Euro-International Concrete Committee (CEB-Comite Euro-International du Beton)-International Federation for Prestressing (FIP-Federation Internationale de la Precontrainte) Model Code and with prediction models found in the literature. In addition, based on the test results, an empirical model of the bond stress-slip relationship was proposed. The evaluation and comparison results showed that the modified CEB-FIP Model code 2010 proposed by Aslani and Samali for the local bond stress-slip relationship for UHPC after exposure to high temperatures was more conservative. In contrast, for both room temperature and after exposure to high temperatures, the modified CEB-FIP Model Code 2010 local bond stress-slip model for UHPC proposed in this study was able to predict the test results with reasonable accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.3-42
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• 2021

Conventional global magnetic field maps, such as daily updated synoptic maps, have been constructed by merging together a series of observations from the Earth's viewing direction taken over a 27-day solar rotation period to represent the full surface of the Sun. It has limitations to predict real-time farside magnetic fields, especially for rapid changes in magnetic fields by flux emergence or disappearance. Here, we construct accurate synchronic magnetic field maps using frontside and AI-generated farside data. To generate the farside data, we train and evaluate our deep learning model with frontside SDO observations. We use an improved version of Pix2PixHD with a new objective function and a new configuration of the model input data. We compute correlation coefficients between real magnetograms and AI-generated ones for test data sets. Then we demonstrate that our model better generate magnetic field distributions than before. We compare AI-generated farside data with those predicted by the magnetic flux transport model. Finally, we assimilate our AI-generated farside magnetograms into the flux transport model and show several successive global magnetic field data from our new methodology.