• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.169-173
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• 2020

The structural analysis was performed at this study when the axle was loaded by using a total of three automotive support beam models, models A, B and C. Comparing with three models A, B, and C, the equivalent stress is considered to be good for its durability because model C is less than the yield stress of the material. The maximum equivalent stresses happening at models A and B are 1.8 times and 2.5 times higher than the yield stress, respectively, indicating that the material is fractured. So, it does not seem to be efficient as a support beam. Model C can be applied efficiently to the improvement design of axle support beams in terms of durability compared to models A and B. The strength of automotive support beam can be evaluated by applying this research result to the automotive part. And it is seen that this study is adequate at the efficient design and aesthetic convergence practically.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.99-105
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• 2019

In this study, the flow analysis around vehicle was carried out on various kinds of roof box models installed at the roof of vehicle. Through the analysis of fluid flow and pressure, we investigated which model was more suitable for driving. The four types of models were designed with their respective shapes of models 1, ${\beta}$, ${\delta}$ and ${\gamma}$, and the driving speed of car was set as 20 m/s. It was confirmed that the pressure for model ${\beta}$ became greatest compared to other models. And model ${\delta}$ has the lowest pressure among all models of roof boxes by installing a canoe with the structure for cable type. As the design data with the durability of roof box obtained on the basis of this study result are utilized, the esthetic sense can be shown by being grafted onto the car body at real life.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.281-286
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• 2019

To repair the underside of the car, a repairman has to enter under the car body. But this work can make it difficult for him to fix it and the injuries can occur. To solve these difficult problems, the developed equipment is the automotive lift. In this study, three kinds of lift models 1, 2 and 3 were designed and the material properties of the structural steel were applied. As the same load were applied under the same conditions on all models, the structural analyses were conducted. Models 2 and 3 were shown to have the structural deformation less than model 1. Also, models 2 and 3 were shown to be more stable than model 1 structurally. By utilizing the design data on a convergence research through durability analysis according to the configuration of automotive lift obtained on the basis of this result, the esthetic feeling can be shown by being converged onto the automotive repair equipment parts at actual life.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• International Journal of Stem Cells
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• v.17 no.1
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• pp.38-50
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• 2024

Induced pluripotent stem cell (iPSC) technology has revolutionized various fields, including stem cell research, disease modeling, and regenerative medicine. The evolution of iPSC-based models has transitioned from conventional two-dimensional systems to more physiologically relevant three-dimensional (3D) models such as spheroids and organoids. Nonetheless, there still remain challenges including limitations in creating complex 3D tissue geometry and structures, the emergence of necrotic core in existing 3D models, and limited scalability and reproducibility. 3D bioprinting has emerged as a revolutionary technology that can facilitate the development of complex 3D tissues and organs with high scalability and reproducibility. This innovative approach has the potential to effectively bridge the gap between conventional iPSC models and complex 3D tissues in vivo. This review focuses on current trends and advancements in the bioprinting of iPSCs. Specifically, it covers the fundamental concepts and techniques of bioprinting and bioink design, reviews recent progress in iPSC bioprinting research with a specific focus on bioprinting undifferentiated iPSCs, and concludes by discussing existing limitations and future prospects.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.105-109
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• 2013

There are the free space model, the direct-path and ground reflected model, Egli model, Okumura-Hata model in the representative propagational models. The measured results at the area of PNG area were used as the experimental data in this paper. The new proposed partial interval analysis method is applied on the measured propagation data in the cellular band. The interval for the analysis is divided from the entire 30 Km distance to 5 Km, and next to 1 Km. The best-fit propagation models are chosen on all partial intervals. The means and standard deviations are calculated for the differences between the measured data and all partial interval models. By using the 5 Km- or 1 Km- partial interval analysis, the standard deviation between the measured data and the partial propagation models was improved more than 1.7 dB.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.2
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• pp.224-232
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• 2000

Four turbulent $k-{\varepsilon}$ models (i.e., standard model, modified models with streamline curvature modification and/or preferential dissipation modification) are applied in order to analyze the turbulent flow of wall-attaching offset jet. For numerical convergence, this paper develops a method of slowly increasing the convective effect induced by skew-velocity in skew-upwind scheme (hereafter called Partial Skewupwind Scheme). Even though the method was simple, it was efficient in view of convergent speed, computer memory storage, programming, etc. The numerical results of all models show good prediction in first order calculations (i.e., reattachment length, mean velocity, pressure), while they show some deviations in ·second order (i.e., kinetic energy and its dissipation rate). Like the previous results obtained by upwind scheme, the streamline curvature modification results in better prediction, while the preferential dissipation modification does not.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.