• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.25 no.4
• /
• pp.315-321
• /
• 2012

In this study, a sequential multiscale homogenization method to characterize the effective thermal conductivity of nano particulate polymer nanocomposites is proposed through a molecular dynamics(MD) simulations and a finite element-based homogenization method. The thermal conductivity of the nanocomposites embedding different-sized nanoparticles at a fixed volume fraction of 5.8% are obtained from MD simulations. Due to the Kapitza thermal resistance, the thermal conductivity of the nanocomposites decreases as the size of the embedded nanoparticle decreases. In order to describe the nanoparticle size effect using the homogenization method with accuracy, the Kapitza interface in which the temperature discontinuity condition appears and the effective interphase zone formed by highly densified matrix polymer are modeled as independent phases that constitutes the nanocomposites microstructure, thus, the overall nanocomposites domain is modeled as a four-phase structure consists of the nanoparticle, Kapitza interface, effective interphase, and polymer matrix. The thermal conductivity of the effective interphase is inversely predicted from the thermal conductivity of the nanocomposites through the multiscale homogenization method, then, exponentially fitted to a function of the particle radius. Using the multiscale homogenization method, the thermal conductivities of the nanocomposites at various particle radii and volume fractions are obtained, and parametric studies are conducted to examine the effect of the effective interphase on the overall thermal conductivity of the nanocomposites.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.593-606
• /
• 2016

Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.

• Journal of Broadcast Engineering
• /
• v.26 no.3
• /
• pp.269-282
• /
• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

• Advances in nano research
• /
• v.16 no.6
• /
• pp.565-577
• /
• 2024

Vibration investigation of fluid-filled functionally graded cylindrical shells with ring supports is studied here. Shell motion equations are framed first order shell theory due to Sander. These equations are partial differential equations which are usually solved by approximate technique. Robust and efficient techniques are favored to get precise results. Employment of the Rayleigh-Ritz procedure gives birth to the shell frequency equation. Use of acoustic wave equation is done to incorporate the sound pressure produced in a fluid. Hankel's functions of second kind designate the fluid influence. Mathematically the integral form of the Langrange energy functional is converted into a set of three partial differential equations. A cylindrical shell is immersed in a fluid which is a non-viscous one. These shells are stiffened by rings in the tangential direction. For isotropic materials, the physical properties are same everywhere where the laminated and functionally graded materials, they vary from point to point. Here the shell material has been taken as functionally graded material. After these, ring supports are located at various positions along the axial direction round the shell circumferential direction. The influence of the ring supports is investigated at various positions. Effect of ring supports with empty and fluid-filled shell is presented using the Rayleigh - Ritz method with simply supported condition. The frequency behavior is investigated with empty and fluid-filled cylindrical shell with ring supports versus circumferential wave number and axial wave number. Also the variations have been plotted against the locations of ring supports for length-to-radius and height-to-radius ratio. Moreover, frequency pattern is found for the various position of ring supports for empty and fluid-filled cylindrical shell. The frequency first increases and gain maximum value in the midway of the shell length and then lowers down. It is found that due to inducting the fluid term frequency result down than that of empty cylinder. It is also exhibited that the effect of frequencies is investigated by varying the surfaces with stainless steel and nickel as a constituent material. To generate the fundamental natural frequencies and for better accuracy and effectiveness, the computer software MATLAB is used.

• Advances in nano research
• /
• v.17 no.3
• /
• pp.213-219
• /
• 2024

Nanotechnology is one of the critical factors involved in enhancing the sensitivity of serum biomarker detection. To explore the relationship between serum APN, Cystatin C and MMP-9 levels in patients with hypertension during pregnancy and the severity and prognosis of the disease. A total of 75 cases of hypertensive disorder complicating pregnancy (HDCP) patients who were admitted to the hospital from February 5, 2023 to May 9, 2024, were selected as the study group, and 70 healthy pregnant women who were in the same gestational week were selected as the control group. The serum APN, MMP-9 and Cys C levels of pregnant women and HDCP patients with different disease severity were compared between the two groups, and the receiver characteristic curve (ROC) was used to analyze its diagnostic value. The serum APN, MMP-9 and Cys C levels of HDCP patients with different prognosis were compared, and the factors affecting the prognosis of patients were analyzed by Logistic regression. Nanoparticles could aslo enable the sensitive detection and quantification of APN, Cystatin C, and MMP-9 in serum samples, thus increasing the accuracy of the study. The serum MMP-9 and Cys C levels of pregnant women in the study group were significantly increased, and the APN level was significantly decreased (P<0.05). Serum MMP-9 and Cys C levels in patients with pregnancy-induced hypertension, mild preeclampsia, and severe preeclampsia gradually increased (r=0.768, 0.766; P<0.001), and APN levels gradually decreased (r=-0.748, P< 0.001). In the diagnosis of patients with HDCP, the sensitivity, specificity and AUC of APN single diagnosis were 70.00%, 82.67% and 9.848 respectively. The sensitivity, specificity and AUC of MMP-9 single diagnosis were 82.86%, 74.67% and 298.300 respectively. The sensitivity, specificity and AUC of Cys C single diagnosis were 80.00%, 74.67% and 1.301 respectively. There were significant differences in age, BMI, parity, dysthymia, disease severity, APN, MMP-9 and Cys between patients with poor prognosis of HDCP and patients with good prognosis of HDCP (P<0.001). The patient's age, BMI, disease severity, APN, MMP-9 and Cys Cwere all related to HDCP. They were related risk factors of HDCP (P<0.05).

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.453-462
• /
• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.