• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.04a
• /
• pp.581-586
• /
• 1996

This paper presents a feature recognition system for recognizing and extracting feature information needed for machining from design data contained in the CAD database of AutoCAD system. The developed system carries out feature recognition from an orthographic view of a press mold containing not only atomic features such as holes, pockets, and slots, but also compound features. Based on the result of feature recognition, it generates a 3-D modeling of the press mold. Especially, The feature recognition part is designed for detecting feature styles according to feature definition and classification, extracting parameters for various atomic features, and constructing necessary data structures for the recognized features.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2002.10a
• /
• pp.344.1-344
• /
• 2002

• Proceedings of the Korean Nuclear Society Conference
• /
• 2003.05a
• /
• pp.89-89
• /
• 2003

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.21 no.1
• /
• pp.175-182
• /
• 2023

APro, a modularized process-based total system performance assessment framework, was developed at the Korea Atomic Energy Research Institute (KAERI) to simulate radionuclide transport considering coupled thermal-hydraulic-mechanical-chemical processes occurring in a geological disposal system. For reactive transport simulation considering geochemical reactions, COMSOL and PHREEQC are coupled with MATLAB in APro using an operator splitting scheme. Conventionally, coupling is performed within a MATLAB interface so that COMSOL stops the calculation to deliver the solution to PHREEQC and restarts to continue the simulation after receiving the solution from PHREEQC at every time step. This is inefficient when the solution is frequently interchanged because restarting the simulation in COMSOL requires an unnecessary setup process. To overcome this issue, a coupling scheme that calls PHREEQC inside COMSOL was developed. In this technique, PHREEQC is called through the "MATLAB function" feature, and PHREEQC results are updated using the COMSOL "Pointwise Constraint" feature. For the one-dimensional advection-reaction-dispersion problem, the proposed coupling technique was verified by comparison with the conventional coupling technique, and it improved the computation time for all test cases. Specifically, the more frequent the link between COMSOL and PHREEQC, the more pronounced was the performance improvement using the proposed technique.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.249-249
• /
• 2012

The interaction of hydrogen with ZnO single crystal surfaces, ZnO(0001) and ZnO(000-1), has been investigated using a temperature programmed desorption (TPD) technique. Both surfaces do not interact with molecular hydrogen. When the ZnO(0001) is exposed to atomic hydrogen at 370 K, hydrogen is adsorbed in the surface and desorption takes place at around 460 K and 700 K. In ZnO(000-1), the desorption peaks are observed at around 440 K and 540 K. In both surfaces, as the atomic hydrogen exposure is further increased, the intensity of the low-temperature peak reaches maximum but the intensity of the high-temperature peak keeps increasing. In ZnO(000-1), the existence of hydrogen bonding to the surface O atoms and the bulk hydrogen has been confirmed by using X-ray photoelectron spectroscopy (XPS). When the Zn(0001) surface is exposed to atomic hydrogen at around 200 K, a new $H_2$ desorption peak has been observed at around 250 K. The intensity of the desorption feature at 250 K is much greater than that of the desorption feature at 460 K. This low-temperature desorption feature indicates hydrogen is bonded to surface Zn atoms. We will report the effect of the ZnO structure on the adsorption and bulk diffusion of hydrogen.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.08a
• /
• pp.216-216
• /
• 2012

The interaction of hydrogen with ZnO single crystal surfaces, ZnO (0001), ZnO (000-1), and ZnO (10-10) has been investigated using temperature programmed desorption (TPD) and X-ray photoelectron Spectroscopy (XPS) techniques. When the ZnO single crystal surfaces are exposed to atomic hydrogen at 200 K, all three surfaces show hydrogen desorption at 450 K. ZnO (0001) surface shows hydrogen desorption feature at ~260 K as the hydrogen exposure is increased. The ZnO (10-10) surface shows low-temperature desorption feature first and the high-temperature desorption feature appears as the hydrogen exposure increases. The ZnO (000-1) surface does not show any lower temperature hydrogen desorption. We will report the adsorption configuration of hydrogen atoms on ZnO single crystal surfaces with different surfaces structures.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.2
• /
• pp.259-266
• /
• 1992

For CAD/CAPP integration, part information on not only geometry but also machining characteristics should be delivered and commonly used between designers and process planners. In this study, the machining features, as linking factors of the integration, are represented as the combination of functional features and atomic features and grouped into a hierarchical database. And the feature based modelling approach is used by generating information on the machining features in design stage. These features are drawn by analyzing real decision rules of process planners. The database using the machining features is built and used for application modules of process planning, operation planning and standard time estimation.

• Journal of Radiation Industry
• /
• v.7 no.2_3
• /
• pp.121-126
• /
• 2013

The new algorithm technique is necessary to incorporate for analyzing and evaluating extreme condition like a nuclear accident. In this study, the combined methodology for measuring the three-dimensional space was compared with SIFT (Scale Invariant Feature Transform) and SURF (Speeded-Up Robust Feature) algorithm. The suggested method can be used for the acquisition of spatial information using the robot vision in the districted area of the nuclear facilities. As a result, these data would be helpful for identify the damaged part, degree of damage and determination of recovery sequences.

• Analytical Science and Technology
• /
• v.13 no.4
• /
• pp.474-483
• /
• 2000

An inductively coupled plasma atomic emission spectrometer/shielding system was specially designed and built for the analysis of radioactive materials. Both of an inductively coupled plasma source and a sample transfer system to be contacted with radioactive materials was installed in a stainless steel glove box. In terms of analytical capability and radiation safety, characteristic feature of the system was investigated. Its applicability to the determination of fission products and corrosion products in the radioactive materials such as spent fuel dissolver solution and the primary coolant of nuclear power reactors was evaluated. In the concentration range $0.01-0.1mgL^{-1}$, the relative standard deviation was found to be less than 5%.

• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.673-687
• /
• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.