• Journal of Korea Foundry Society
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• v.20 no.5
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• pp.330-335
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• 2000

Functional metal prototypes are often required in numerous industrial applications. These components are typically needed in the early stage of a project to determine form, fit and function. Recent R/P(Rapid Prototyping) part are made of soft materials such as plastics, wax, paper, these master models cannot be employed durable test in real harsh working environment. Parts by direct metal rapid tooling method, such as laser sintering, by now are hard to get net shape, pores of the green parts of powder casting method must be infiltrated to get proper strength as tool, and new type of 3D direct tooling system combining fabrication welding arc and cutting process is reported. But a system which can build directly 3D parts of high performance functional material as metal park would get long period of system development, massive investment and other serious obstacles, such as patent. In this paper, through the rapid tooling process as silicon rubber molding using R/P master model, and fabricate wax pattern in that silicon rubber mold using vacuum casting method, then we translated the wax patterns to numerous metal tool prototypes by new investment casting process combined conventional investment casting with rapid prototyping & rapid tooling process. With this wax-injection-mold-free investment casting, we developed new investment casting process of fabricating numerous functional metal prototypes from one master model, combined 3-D CAD, R/P and conventional investment casting and tried to expect net shape measuring total dimension shrinkage from R/P pare to metal part.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Coupled systems mechanics
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• v.11 no.1
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• pp.43-54
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• 2022

In order to analyse the thermal performance of battery systems in electric vehicles complex simulation models with high computational cost are necessary. Using reduced order methods, real-time applicable model can be developed and used for on-board monitoring. In this work a data driven model of the cooling plate as part of the battery system is built and derived from a computational fluid dynamics (CFD) model. The aim of this paper is to create a meta model of the cooling plate that estimates the temperature at the boundary for different heat flow rates, mass flows and inlet temperatures of the cooling fluid. In order to do so, the cooling plate is simulated in a CFD software (ANSYS Fluent ®). A data driven model is built using the design of experiment (DOE) and various approximation methods in Optimus ®. The model can later be combined with a reduced model of the thermal battery system. The assumption and simplification introduced in this paper enable an accurate representation of the cooling plate with a real-time applicable model.

• MALSORI
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• no.62
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• pp.97-112
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• 2007

The speech recognizer in general operates in noisy acoustical environments. Many research works have been done to cope with the acoustical variations. Among them, the multiple-HMM model approach seems to be quite effective compared with the conventional methods. In this paper, we consider a multiple-model approach combined with the model compensation method and investigate the necessary number of the HMM model sets through noisy speech recognition experiments. By using the data-driven Jacobian adaptation for the model compensation, the multiple-model approach with only a few model sets for each noise type could achieve comparable results with the re-training method.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.331-338
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• 2001

In this paper, we have proposed a new computer retina model reflecting the mechanism of transient amacrine cell on the basis of a conventional computer retina model to understand mechanism of visual information processing. The conventional computer retina model contained most of mechanism for other retina models and it was verified with the physiological data. However, we found that a conventional computer retina model doesn't have the mechanism of amacrine cell that was likely to respond to moving stimulus. In proposed model, therefore, a conventional computer model that considered from photoreceptors to bipolar cells and a new computer model that considered for transient amacrine cell and ganglion cell was combined. As we compared the physiological data with the results of computer simulation of transient amacrine cell about fixed stimulus and moving stimulus, we confirmed that the proposed new computer retina model was normally operated.

• Speech Sciences
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• v.9 no.2
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• pp.135-146
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• 2002

Generally a speaker verification system improves its system recognition ratio by regularizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. The speaker-based cohort method is one of the methods that are widely used for selecting background speaker model. Recently, Gaussian-based cohort model has been suggested as a virtually synthesized cohort model, and unlike a speaker-based model, this is the method that chooses only the probability distributions close to basic speaker's probability distribution among the several neighboring speakers' probability distributions and thereby synthesizes a new virtual speaker model. It shows more excellent results than the existing speaker-based method. This study compared the existing speaker-based background speaker models and virtual speaker models and then constructed new virtual background speaker model groups which combined them in a certain ratio. For this, this study constructed a speaker verification system that uses GMM (Gaussin Mixture Model), and found that the suggested method of selecting virtual background speaker model shows more improved performance.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.149-157
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• 1994

In this study two dimensional visco-plastic finite element model capable of handling the multi-step excavation was developed for investigating the effect of excavation support sequences on the behavior of underground openings in the jointed rock mass. First, the finite element model which is capable of handling the multi-step excavation is developed and verified. And then the model is combined with visco-plastic joint model. Ubiquitous joint pattern was considered in the model and joint properties in cach set were assumed to be indentical. Passive, full-grouted rockbolts were cosidered in the numerical model. The visco-plastic deformations of joints and rockbolts were assumed to be governed by Mohr-Conlomb and von Mises yield criteria, respectively. With the ability of removing elements, the model can simulate the multi-step excavation-suppport sequences. The reliability and applicability of the model to the stability analysis for the underground excavation in pratice was checked by simulating the behavior of underground crude oil storage caverns under construction.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.354-361
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• 2002

This paper propose section properties factor to generate stress history for fatigue analysis and safety inspection of steel bridge. A methodology is described for the computation of numerical stress histories in the steel truss bridge, caused by the vehicles using section properties factor. The global 3-D beam model of bridge is combined with the local shell model of selected details. Joint geometry is introduced by the local shell model. The global beam model takes the effects of joint rigidity and interaction of structural elements into account. Connection nodes in the global beam model correspond to the end cross-section centroids of the local shell model. Their displacements are interpreted as imposed deformations on the local shell model. The load cases fur the global model simulate the vertical unit force along the stringers. The load cases fer the local model are imposed unit deformations. Combining these, and applying vehicle loads, numerical stress histories are obtained. The method is illustrated by test load results of an existing bridge.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.229-236
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• 2004

Radiation hardening is a multiscale phenomenon involving various processes over a wide range of time and length. We present a multiscale model for estimating the amount of radiation hardening in pressure vessel steel in the environment of a light water reactor. The model comprises two main parts: molecular dynamics (MD) simulation and a point defect cluster (PDC) model. The MD simulation was used to investigate the primary damage caused by displacement cascades. The PDC model mathematically formulates interactions between point defects and their clusters, which explains the evolution of microstructures. We then used a dislocation barrier model to calculate the hardening due to the PDCs. The key input for this multiscale model is a neutron spectrum at the inner surface of reactor pressure vessel steel of the Younggwang Nuclear Power Plant No.5. A combined calculation from the MD simulation and the PDC model provides a convenient tool for estimating the amount of radiation hardening.