• Journal of radiological science and technology
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• v.31 no.2
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• pp.123-127
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• 2008

Purpose: As the demand of a simple and precise method increases to evaluate the performance of the inverter type x-ray unit, we evaluated the usefulness of the recently-introduced X-ray Multi-Function Test Device (moldel : Xi (unfors)-prestige). Method: We compared the performance of X-ray Multi-Function Test Device (XMFTD) which is non-inveasive type device with the performance of Dynalyzer III that has been most widely used inveasive type measure device. Result: X-ray output dose was increased a little in the XMFTD, but both devices were below the performance evaluation standard, 0.002 in the output reproducibility. Linearity of XMFTD were below 0.1 which means that Dynalyzer III showed more excellency in linearity. As the the accuracy of exposure factor, 1.8 and 2 tube voltage, 2.01 and 2.3 tube current were measured. The exposure time was also measured by 0.01 sec ${\pm}10%$. Both devices were within the acceptance of performance evaluatioin standard. Conclusion: We proved the usefulness of X-ray Multi-Function Test Device (model: Xi (unfors)-prestige) to evaluated the performance on reproductibility and linearity of X-ray output and accuracy of exposure factor of inverter type unit.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.289-293
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• 2016

As more mobile devices are equipped with multi-core CPUs and are required to execute many compute-intensive multimedia applications, it is important to optimize the systems, considering the underlying parallel hardware architecture. In this paper, we implement and optimize ray-tracing application tailored to a given mobile computing platform with multiple heterogeneous processing elements. In this paper, a lightweight ray-tracing application is specified and implemented in Kahn process network (KPN) model-of-computation, which is known to be suitable for the description of real-time applications. We take an open-source C/C++ implementation of ray-tracing and adapt it to KPN description in the Distributed Application Layer framework. Then, several possible configurations are evaluated in the target mobile computing platform (Exynos 5422), where eight heterogeneous ARM cores are integrated. We derive the optimal degree of parallelism and a suitable distribution of the replicated tasks tailored to the target architecture.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.173-178
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• 2020

A multi-ray model has been used to interpret radio transmission losses in half-canyon structures with slope and to formulate a multi-ray propagation model depending on the angle of slopes. The cut-off angles for the third and fourth paths, which are the slope-sided reflection paths of the transmission and reception radio waves determined by the inclined angles of the slope, were calculated with the height and location of the transmitter and receiver. To predict transmission losses in an inclined plane environment, the embankment environment where the actual slope exists was modeled and simulated to calculate the loss of propagation transmission, and the radio wave transmission loss was confirmed by the measurement for the frequency band 1 to 6 GHz. Simulation results and measurement results showed similar trends in radio transmission loss, and radio transmission loss predictions and measurement results for various terrain information can be used in the design of radio propagation service.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.679-685
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• 2002

Low-frequency mono-static reverberation model for shallow-water environment is presented. It is necessary to develop the transmission loss model to calculate the sub-bottom interaction because the ray-based transmission loss model is difficult to compute the pressure accurately which penetrates the bottom medium. In this paper reverberation level is calculated using the RAM (Range dependent Acoustic Model) to augment the multi-path expansion model because it does not estimate transmission loss accurately in shallow water. The signals generated by the L-HYREV and the GSM are compared with the observed signals and it is showed that the L-HYREV model provides a closer fit to the observed signals than those obtained using the GSM.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.209-214
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• 2014

In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

• Transactions of Materials Processing
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• v.19 no.4
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• pp.224-229
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• 2010

During the multi-pass wire drawing process, wires suffer a great amount of plastic deformation that is through the cross-section. This generates tensile residual stress at surface of drawn wires. The generated residual stress on surface is one of the problems for quality of wires so that prediction and reduction of residual stresses is important to avoid unexpected fracture. Therefore, in this study, the effect of process variables such as semi-die angle, bearing length and reduction ratio on the residual stress was evaluated through Finite Element Analysis. Based on the results of the Analysis, a prediction model was established for predicting residual stress on the surface of high carbon steel(AISI1072, AISI1082). To identify the effectiveness of the proposed model, X-ray diffraction is used to measure the residual stresses on the surface. As the result of the comparison between calculated residual stresses and measured residual stresses, the model could be used to predict residual stresses in cold drawn wire.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.8
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• pp.642-653
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• 2008

In this paper, we propose a 3-D finite element (FE) analysis model with combined physical behavior and kinematical impact factors for evaluation of residual stress in multi-impact shot peening. The FE model considers both physical behavior of material and characteristics of kinematical impact. The physical parameters include elastic-plastic FE modeling of shot ball, material damping coefficient, dynamic friction coefficient. The kinematical parameters include impact velocity and diameter of shot ball. Multi-impact FE model consists of 3-D symmetry-cell. We can describe a certain repeated area of peened specimen under equibiaxial residual stress by the cell. With the cell model, we investigate the FE peening coverage, dependency on the impact sequence, effect of repeated cycle. The proposed FE model provides converged and unique solution of surface stress, maximum compressive residual stress and deformation depth at four impact positions. Further, in contrast to the rigid and elastic shots, plastically deformable shot produces residual stresses closer to experimental solutions by X-ray diffraction. Consequently, it is confirmed that the FE model with peening factors and plastic shot is valid for multi-shot peening analyses.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.151-156
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• 2017

Shot peening is widely used to improve the fatigue life and strength of various mechanical parts and an accurate method is important for the prediction of the compressive residual stress caused by this process. A finite element (FE) model with an elliptical multi-shot is suggested for random-angled impacts. Solutions for compressive residual stress using this model and a normal random vertical-impact one with a spherical multi-shot are obtained and compared. The elliptical multi-shot experimental solution is closer to an X-ray diffraction (XRD) than the spherical one. The FE model's peening coverage also almost reaches the experimental one. The effectiveness of the model based on an elliptical shot ball is confirmed by these results and it can be used instead of previous FE models to evaluate the compressive residual stress produced on the surface of metal by shot peening in various industries.

• Proceedings of the IEEK Conference
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• summer
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• pp.361-364
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• 2004

This paper describes an automatic 3D models generation algorithm based on 2D silhouette images, using X-ray camera without camera parameters. The algorithm takes a multi steps process approach. First, a series of 2D silhouette images is captured from different directions of object and then converted to binary images. An octree data structure is constructed for voxel-based representation of object. An estimate 3D volume of object can be reconstructed by intersecting voxels and the 2D silhouettes. The marching cube algorithm is applied to get triangle mesh representing of the obtained 3D model for rendering.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.