• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.735-745
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• 1997

In this study, the compressibility of resin was considered in filling analysis to account for the possible packing type flow. A numerical simulation program employing a hybrid finite element/finite difference scheme was developed to solve Hele-Shaw flow of the compressible viscous fluid at non-isothermal conditions. To advance the melt front, a control volume approach was adopted. Thin complex 3-D shapes of cavities, runners, and sprues were discretized by employing triangular, cylindrical and/or rectangular strip elements. Mass conservation was applied to each control volume to solve for the pressure distribution. Directly applying a constant mass flow rate at the inlet removes calculation of the apparent pressure boundary conditions, resulting in better simulation condition. The Cross model was used to model viscosity and the Tait equation was employed to represent density as a function of temperature and pressure. The validity of the developed program was verified through comparisons with available data in the literature and the effect of compressibility on the pressure distribution was discussed. To reduce computation time, 1-D and 2-D elements were used instead of applying triangular elements and the numerical results were compared to each other.

• Journal of Hospice and Palliative Care
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• v.24 no.3
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• pp.184-193
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• 2021

Purpose: This study attempted to develop clinical guidelines to help patients use hospice and palliative care (HPC) at an appropriate time after writing physician orders for life-sustaining treatment (POLST) by identifying the characteristics of HPC use of patients with terminal cancer. Methods: This retrospective study was conducted to understand the characteristics of HPC use of patients with terminal cancer through decision tree analysis. The participants were 394 terminal cancer patients who were hospitalized at a cancer-specialized hospital in Seoul, South Korea and wrote POLST from January 1, 2019 to March 31, 2021. Results: The predictive model for the characteristics of HPC use showed three main nodes (living together, pain control, and period to death after writing POLST). The decision tree analysis of HPC use by terminal cancer patients showed that the most likely group to use HPC use was terminal cancer patients who had a cohabitant, received pain control, and died 2 months or more after writing a POLST. The probability of HPC usage rate in this group was 87.5%. The next most likely group to use HPC had a cohabitant and received pain control; 64.8% of this group used HPC. Finally, 55.1% of participants who had a cohabitant used HPC, which was a significantly higher proportion than that of participants who did not have a cohabitant (1.7%). Conclusion: This study provides meaningful clinical evidence to help make decisions on HPC use more easily at an appropriate time.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.484-495
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• 2020

To numerically simulate the advance of EPB TBM, various type of numerical analysis methods have been adopted including discrete element method (DEM), finite element method (FEM), and finite difference method (FDM). In this paper, an EPB TBM driving model was proposed by using coupled DEM-FDM. In the numerical model, DEM was applied in the TBM excavation area, and contact properties of particles were calibrated by a series of triaxial tests. Since the ground around the excavation area was coupled with FDM, the horizontal stress considering the coefficient of earth pressure at rest could be applied. Also, the number of required particles was reduced and the efficiency of the analysis was increased. The proposed model can control the advance rate and rotational speed of the cutter head and screw conveyor, and derive the torque, thrust force, chamber pressure, and discharging during TBM tunnelling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.215-217
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• 2014

A system architecture design relying only on the experience of its designer varies in quality in line with the designer's personal experience and knowledge ability. Likewise, a wrong estimation of hardware capacity ends up in waste of resources. In practice, a range of post-hoc monitoring tools are in operation, without providing any method for estimating and reflecting the performance at an early stage of architecture design. Provided capacity requirement is estimated in advance with simulation at the stage of design, the system capacity ends up in waste of resources. In practice, a range of post-hoc monitoring tools are in operation, without providing any method for estimating and reflecting the performance at an early stage of architecture design. Provided capacity requirement is estimated in advance with simulation at the stage of design, the system performance requirement can be met with a minimal cost while the waste of resources can be reduced to a great extent. In this context, the present study develops a pilot simulation model for hardware architecture design and then verifies its validity in an experiment. If the error rate falls within a permissible range in the experiment, the simulation model may be considered to reflect well the characteristics of real-life information system architecture.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.263-268
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• 2021

The purpose of this study was to propose a model which is suitable for the actual delivery system by designing a fetal delivery hospital operation management and fetal health classification model. The number of deaths during childbirth is similar to the number of maternal mortality rate of 295,000 as of 2017. Among those numbers, 94% of deaths are preventable in most cases. Therefore, in this paper, we proposed a model that predicts the health condition of the fetus using data like heart rate of fetuses, fetal movements, uterine contractions, etc. that are extracted from the Cardiotocograms(CTG) test using a random forest. If the redundancy of the data is unbalanced, This proposed model guarantees a stable management of the fetal delivery health management system. To secure the accuracy of the fetal delivery health management system, we remove the outlier which embedded in the system, by setting thresholds for the upper and lower standard deviations. In addition, as the proportion of the sequence class uses the health status of fetus, a small number of classes were replicated by data-resampling to balance the classes. We had the 4~5% improvement and as the result we reached the accuracy of 97.75%. It is expected that the developed model will contribute to prevent death and effective fetal health management, also disease prevention by predicting and managing the fetus'deaths and diseases accurately in advance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.4
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• pp.343-349
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• 2007

The reduced scale fire test provides basic data but it is not enough to analysis real fire problem directly because there is no exact analogy theory between a real fire and the reduced scale model. Therefore we have developed the large scale calorimeter in order to the real scale fire test. This advanced large scale calorimeter used for physical properties such as a heat release rate, based upon consumption of $O_2$ method. Using this large scale calorimeter, we cameo out the real scale vehicle fire test in order to evaluation for heat release rate. We obtained the calculated result for HRR $2.3{\sim}3.4\;MW$ and this result is very similar to the PIARC candidate HRR. It is approve that this facility has the reliability and it is capable of applying to the advance fire research in the future.

• Journal of IKEEE
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• v.22 no.2
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• pp.274-280
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• 2018

In this paper, we have developed the vacuum infusion automation system for the safety and quality advancement of the boat. It is necessary for the precise mixing rate of resinoid and curingagent to inject in an inner ship and deck at short time. We need for the optimal condition to a strengthen construction of boat. This one can solve the post deformability of the strengthen structure and can control the precise mixing rate of resinoid and curingagent to the resinoid fluidity and flowing rate per time. Under these condition, we can advance the an quality construction that based on the model and database information of the boat. Also, we can have an effective process management and retrench the production cost.