• The Korea Genome Conference
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• 2002.08a
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• pp.42.2-42.2
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• 2002

• Journal of Biosystems Engineering
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• v.36 no.2
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• pp.130-139
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• 2011

Three modes of VIS/NIR spectroscopic measurement (interactance and two modes of transmission) were compared for their ability to estimate soluble solids content (SSC) of 'Shingo' pear non-destructively. The two transmission modes are named as full- and semi-transmission, where full-transmission stands for passing of light through abdomen of pear and semi-transmission is for transit of light mainly through flesh of pear. For comparison of the modes, prediction models developed from the collected spectroscopic data by the three modes were developed and tested for comparison of their performance. Partial least square regression (PSLR) was used to develop the models and various pre-processing methods were applied to develop models of high accuracy. The experiment was repeated three times with pears produced in different regions. The experiments resulted that selection of pre-processing is very important to attain accurate models, and multiplicative scatter correction (MSC) was selected as a pre-processor of high accuracy for the three modes of spectroscopic measurement in every experiment. Except for MSC, different group of pre-processing methods were selected for the three modes of measurement in every experiment without any tendency to the tested modes of measurement and pears of different produced region. Root-mean-square error of prediction (RMSEP) of prediction models of the three modes of measurement using prepreocessor of MSC were compared for their ability to estimate SSC. The models resulted in ranges of $0.37{\sim}0.57^{\circ}Brix$, $0.65{\sim}0.72^{\circ}Brix$, $0.39{\sim}0.51^{\circ}Brix$ for interactance, full- and semi-transmission, respectively. As shown, modes of semi-transmission and interactance resulted about the same level of prediction accuracy and were noted as modes of high performance to predict SSC.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.402-412
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• 2020

Objective: To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. Materials and Methods: We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. Results: Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. Conclusion: Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.61-66
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• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.283-290
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• 2021

We propose a task and motion planning algorithm for clearing obstacles and wiping surfaces, which is essential for surface disinfection during the pathogen disinfection process. The proposed task and motion planning algorithm determines task parameters such as grasping pose and placement location during the planning process without using pre-specified or discretized values. Furthermore, to quickly inspect many unit motions, we propose a motion feasibility prediction algorithm consisting of collision checking and an SVM model for inverse mechanics and self-collision prediction. Planning time analysis shows that the feasibility prediction algorithm can significantly increase the planning speed and success rates in situations with multiple obstacles. Finally, we implemented a hierarchical control scheme to enable wiping motion while following a planner-generated joint trajectory. We verified our planning and control framework by conducted an obstacle-clearing and surface wiping experiment in a simulated disinfection environment.

• Annual Conference of KIPS
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• 2021.05a
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• pp.323-325
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• 2021

In this article, we have improved the prediction of hypertension detection using the feature selection method for the Korean national health data named by the KNHANES database. The study identified a variety of risk factors associated with chronic hypertension. The paper is divided into two modules. The first of these is a data pre-processing step that uses a factor analysis (FA) based feature selection method from the dataset. The next module applies a predictive analysis step to detect and predict hypertension risk prediction. In this study, we compare the mean standard error (MSE), F1-score, and area under the ROC curve (AUC) for each classification model. The test results show that the proposed FIFA-OE-NB algorithm has an MSE, F1-score, and AUC outcomes 0.259, 0.460, and 64.70%, respectively. These results demonstrate that the proposed FIFA-OE method outperforms other models for hypertension risk predictions.

• Corrosion Science and Technology
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• v.4 no.4
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• pp.136-139
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• 2005

The effect of oxidation and hot corrosion on the solid particle erosion was investigated for hot-pressed silicon nitride using as-polished, pre-oxidized and pre-corroded specimens by molten sodium sulfates. Erosion tests were performed at 22, 500 and $900^{\circ}C$ using angular silicon carbide particles of mean diameter $100{\mu}m$. Experimental results show that solid particle erosion rate of silicon nitride increases with increasing temperature for as-polished or pre-oxidized specimens in consistent with the prediction of a theoretical model. Erosion rate of pre-oxidized specimens is lower than that of as-polished specimens at $22^{\circ}C$, but it is higher at $900^{\circ}C$. Lower erosion rate at $22^{\circ}C$ in the pre-oxidized specimens is attributed due to the blunting of surface flaws, and the higher erosion rate at $900^{\circ}C$ is due to brittle lateral cracking. Erosion rate of pre-corroded specimens decreases with increasing temperature. Less erosion at $900^{\circ}C$ than at $22^{\circ}C$ is associated with the liquid corrosion products sealing off pores at $900^{\circ}C$ and the absence of inter-granular crack propagation observed at $22^{\circ}C$.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.170-180
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• 2008

Successful design, construction and maintenance of NATM tunnel demands prediction, control, stability guidelines, the estimation pre-improvement support system and monitoring of surface settlement, gradient and ground displacement with high accuracy. Moreover, urban NATM tunnel under difficult geotechnical conditions is important the estimation and necessary of pre-improvement support system. Various strategies have been proposed for the quantitative estimation of pre-improvement support system. This paper was investigated and analysed an assessment technique for the quantitative estimation of pre-improvement support system on underground space, as mountain and urban tunnel, in detail. The analysis performed on design and construction stage with field database using the proposed stability estimation index by many researcher including the critical strain and the apparent Young's modulus concept.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.772-779
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• 2019

The subsea pipeline has received considerable attention as a high-value-added industry linked to the energy and steel industries including natural resource development. The design and installation of the subsea pipeline require a variety of key technologies to carry out the project. In particular, a thorough pre-verification process through pre-commissioning is essential for the safe operation of the subsea pipeline. The hydrotesting stage in the pre-commissioning process of the subsea pipeline is known to be affected significantly by the ambient temperature change; however, there is a little study based on the theoretical and numerical approach. In this study, the method of predicting the internal temperature change using the transient heat transfer method for the stage of hydrotesting during the pre-commissioning process of the subsea pipeline and the prediction method of the pressure variation in the pipeline using it were proposed. The predicted results were compared with field test results and its effectiveness was verified. The proposed analysis procedure is expected to contribute to the productivity improvement of the subsea pipeline installation project by enabling the prediction of pressure variation through pipeline heat transfer simulation from the initial design stage of the subsea pipeline installation project.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.