• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1169-1176
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• 2000

In this paper, label assignment schemes considering the IP flow model for the efficient MPLS traffic engineering are proposed and evaluated. Based on the IP flow model, the IP flows are classified into transient flows and base flows. Base flows, which last for a long time, transmit data in high bit rate, and be composed of many packets, have good implications for the MPLS traffic engineering, because they usually cause network congestion. To make use of base flows for the MPLS traffic engineering, we propose two base flow classifiers and label assignment schemes where transient flows are allocated to the default LSPs and base flows to explicit LSPs. Proposed schemes are based on the traffic-driven label triggering method combined with a routing tabel. The first base flow classifier uses both flow size in packet counts and routing entries, and the other one, extending the dynamic X/Y flow classifier, is based on a cut-through ratio. Proposed schemes are shown to minimize the number of labels, not degrading the total cut-through ratio.

• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.349-360
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• 2012

A horizontally curvilinear non-hydrostatic free surface model that was applicable to three-dimensional viscous flows was developed. The proposed model employed a top-layer equation to close kinematic free-surface boundary condition, and an isotropic k-${\varepsilon}$ model to close turbulence viscosity in the Reynolds averaged Navier-Stokes equation. The model solved the governing equations with a fractional step method, which solved intermediate velocities in the advection-diffusion step, and corrects these provisional velocities by accounting for source terms including pressure gradient and gravity acceleration. Numerical applications were implemented to the wind-driven currents in a two-dimensional closed basin, the flow in a steep-sided trench, and the flow in a strongly-curved channel accounting for secondary current by the centrifugal force. Through the numerical simulations, the model showed its capability that were in good agreement with experimental data with respect to free surface elevation, velocity, and turbulence characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.505-516
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• 2018

In order to establish an efficient bridge maintenance strategy, the future performance of a bridge must be estimated by considering the current performance, which allows more rational way of decision-making in the prediction model with higher accuracy. However, personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. Therefore, in this work, we developed a representative performance prediction model for each bridge element considering uncertainty using domestic bridge inspection data, and proposed a bayesian updating method that can apply the developed model to actual maintenance bridge with higher accuracy. Also, the feasibility analysis based on calculation of maintenance cost for monitoring maintenance scenario case is performed to propose advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

• Proceedings of the KSRS Conference
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• v.1
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• pp.21-24
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• 2006

Processes controlling the interannual variation of mixed layer temperature (MLT) averaged over the NINO3 domain ($150-90^{\circ}W$, $5^{\circ}N-5^{\circ}S$) are studied using an ocean data assimilation product that covers the period of 1993 to 2003. Advective tendencies are estimated here as the temperature fluxes through the domain's boundaries, with the boundary temperature referenced to the domain-averaged temperature to remove the dependence on temperature scale. The overall balance is such that surface heat flux opposes the MLT change but horizontal advection and subsurface processes assist the change. The zonal advective tendency is caused primarily by large-scale advection of warm-pool water through the western boundary of the domain. The meridional advective tendency is contributed mostly by Ekman current advecting large-scale temperature anomalies though the southern boundary of the domain. Unlike many previous studies, we explicitly evaluate the subsurface processes that consist of vertical mixing and entrainment. In particular, a rigorous method to estimate entrainment allows an exact budget closure. The vertical mixing across the mixed layer (ML) base has a contribution in phase with the MLT change. The entrainment tendency due to temporal change in ML depth is negligible comparing to other subsurface processes. The entrainment tendency by vertical advection across the ML base is dominated by large-scale changes in wind-driven upwelling and temperature of upwelling water. Tropical instability waves (TIWs) result in smaller-scale vertical advection that warms the domain during La Ni? cooling events. When the advective tendencies are evaluated by spatially averaging the conventional local advective tendencies of temperature, the apparent effects of currents with spatial scales smaller than the domain (such as TIWs) become very important as they redistribute heat within the NINO3 domain. However, such internal redistribution of heat does not represent external processes that control the domain-averaged MLT.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.271-280
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• 2019

Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.3
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• pp.21-30
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• 2018

Driven by the recent energy saving trend, conventional silicon based power conversion modules are being replaced by modules using silicon carbide. Previous papers have focused mainly on the electrical advantages of silicon carbide semiconductors that can be used to design switching devices with much lower losses than conventional silicon based devices. However, no systematic study of their thermomechanical reliability in power conversion modules using finite element method (FEM) simulation has been presented. In this paper, silicon and silicon carbide based power devices with three-phase switching were designed and compared from the viewpoint of thermomechanical reliability. The switching loss of power conversion module was measured by the switching loss evaluation system and measured switching loss data was used for the thermal FEM simulation. Temperature and stress/strain distributions were analyzed. Finally, a thermal fatigue simulation was conducted to analyze the creep phenomenon of the joining materials. It was shown that at the working frequency of 20 kHz, the maximum temperature and stress of the power conversion module with SiC chips were reduced by 56% and 47%, respectively, compared with Si chips. In addition, the creep equivalent strain of joining material in SiC chip was reduced by 53% after thermal cycle, compared with the joining material in Si chip.