• Investigative Magnetic Resonance Imaging
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• v.17 no.2
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• pp.123-132
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• 2013

Purpose : We report the results of the various parameters of diffusion tensor imaging (DTI) and CSF flow study of the cervical spinal cord using magnetic resonance (MR) imaging techniques. Materials and Methods: Intramedullary FA and MD were measured in the gray matter and posterior cord of the white matter and both lateral cords of the white matter at the C2-3, C4-5, C5-6 spinal levels. For the CSF flow study, velocity encoding was obtained at the C2-3, C4-5, C5-6 spinal levels. Results: There was a significant difference of the FA and MD between the white matter and gray matter (p < 0.05). The FA of the gray matter was significantly different according to the cervical spinal cord levels (p < 0.05). Otherwise, the FA and MD parameters were not significantly different (p > 0.05). The mean peak systolic velocity and mean peak diastolic velocity were $5.18{\pm}2.00cm/sec$ and $-7.32{\pm}3.18cm/sec$, respectively from C2 to C6 spinal cords. There was no significant difference in these velocities among the cervical spinal cord (p > 0.05). Conclusion: This basic information about DTI and CSF dynamics of the cervical spinal cord may be useful for assessing cervical spinal cord abnormalities using MR imaging.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.4
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• pp.467-474
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• 2004

A large-eddy simulation is performed for turbulent flow in a concentric annulus with the inner wall rotation at Re$\sub$Dh/=8900 for three rotation rates N=0.2145, 0.429 and 0.858. Main emphasis is placed on the inner wall rotation effect on near-wall turbulent structures. Near-wall turbulent structures close to the inner wall are scrutinized by computing the lower-order statistics. The anisotropy invariant map for the Reynolds stress tensor and the invariant function are illustrated to reveal the altered anisotropy in turbulent structure. Probability density functions of the splat/anti-splat process are explored to develop a sufficiently complete picture of the contributions of the flow events to turbulent production. The present numerical results show that the altered turbulent structures may be attributed to the centrifugal instability, which leads to the augmentation of sweep and ejection events.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.9
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• pp.741-753
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• 2007

The elliptic conceptual second moment model for turbulent heat fluxes, which was proposed on the basis of elliptic-relaxation equation, was applied to calculate the turbulent heat transfer in an axially rotating pipe flow. The model was closely linked to the elliptic blending model which was used for the prediction of Reynolds stress. The effects of rotation on the turbulent characteristics including the mean velocity, the Reynolds stress tensor, the mean temperature and the turbulent heat flux vector were examined by the model. The numerical results by the present model were directly compared to the DNS as well as the experimental results to assess the performance of the model predictions and showed that the behaviors of the turbulent heat transfer in the axially rotating pipe flow were satisfactorily captured by the present models.

• International Journal of Fluid Machinery and Systems
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• v.5 no.2
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• pp.49-59
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• 2012

The present study is devoted to the influence of a superposed radial inflow in a rotor-stator cavity with a peripheral opening. The flow regime is turbulent, the two boundary layers being separated by a core region. An original theoretical solution is obtained for the core region, explaining the reason why a weak radial inflow has no major influence near the periphery of the cavity but strongly affects the flow behavior near the axis. The validity of the theory is tested with the help of a new set of experimental data including the radial and tangential mean velocity components, as well as three components of the Reynolds stress tensor measured by hot-wire anemometry. The theoretical results are also in good agreement with numerical results obtained with the Fluent code and experimental data from the literature.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.473-478
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• 2000

In this study, the characteristics of the three-dimensional turbulent flow in a rotating square sectioned $90^{\circ}$ bend were investigated by numerical simulation and experiment. In the experimental study, the characteristics of a developing turbulent flow are measured using hot-wire anemometer to seize the rotational effects on the flow characteristics and to compare the results of computational simulation with Reynolds stress model. Each refinement is shown to lead to an appreciable improvement in the agreement between measurement and computation.

• Bulletin of the Korean Chemical Society
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• v.21 no.9
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• pp.875-881
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• 2000

We study the rheological properties of concentrated polymer solution and the melt under simple shear and elon-gational flow using Brownian dynamicssimulation. In order to describe the anisotropic molecular motion, we modifiedthe Giesekus' mobility tensor by incorporating the finitely extensible non-linear elastic (FENE) spring force into dumbbell model. To elucidate the nature of this model, our simulation results are compared with the data of FENE-P ("P"standsfor the Perterin) dumbbell model and experiments. While in steady state both original FENE and FENE-P models exhibit a similar viscosity response,the growthof viscosity becomes dissimilar as the anisotropy decreases and the flowrate increases. The steady state viscosity obtained from the simulation well describes the experiments including the shear-thinning behavior in shear flow and viscosity-thinning behavior in elongational flow. But the growth of viscosity oforiginal FENE dumbbell model cannot describe the experimental results in both flow fields.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.201-207
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• 2021

Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• 한국전산유체공학회:학술대회논문집
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• 2009.11a
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• pp.88-92
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• 2009

When exposed to uniform magnetic fields externally applied, paramagnetic particles acquire dipole moments and the induced moments interacting with each other lead to the formation of chainlike structures or clusters of particles aligned with the field direction. A direct simulation method, based on the Maxwell stress tensor and a fictitious domain method, is applied to solve flows with magnetic chains in simple shear flow. We assumed that the particles constituting the chains are paramagnetic, and inertia of both flow and magnetic particles is negligible. The numerical scheme enables us to take into account both hydrodynamic and magnetic interactions between particles in a fully coupled manner, enabling us to numerically visualize breakup and reformation of the chains by the combined effect of the external field and the shear flow. Simple shear flow with suspended magnetic chains is solved in a periodic domain for a given magnetic field. Dynamics of interacting magnetic chains is found to be significantly affected by a dimensionless parameter called the Mason number, the ratio of the viscous force to the magnetic force in the shear flow. The effect of particle area fraction on the chain dynamics is investigated as well.