• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.1-9
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• 2023

For optimal image quality of computer tomography angiography (CTA), different iodine concentrations and scan parameters were applied to quantitatively evaluate the image quality characteristics of filtered back projection (FBP), hybrid-iterative reconstruction (hybrid-IR), and deep learning reconstruction (DLR). A 320-row-detector CT scanner scanned a phantom with various iodine concentrations (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4, and 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm. Data obtained using each reconstruction technique was analyzed through noise, coefficient of variation (COV), and root mean square error (RMSE). As the iodine concentration increased, the CT number value increased, but the noise change did not show any special characteristics. COV decreased with increasing iodine concentration for FBP, adaptive iterative dose reduction (AIDR) 3D, and advanced intelligent clear-IQ engine (AiCE) at various tube voltages and tube currents. In addition, when the iodine concentration was low, there was a slight difference in COV between the reconstitution techniques, but there was little difference as the iodine concentration increased. AiCE showed the characteristic that RMSE decreased as the iodine concentration increased but rather increased after a specific concentration (4.9 mg/mL). Therefore, the user will have to consider the characteristics of scan parameters such as tube current and tube voltage as well as iodine concentration according to the reconstruction technique for optimal CTA image acquisition.

• Design & Manufacturing
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• v.17 no.1
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• pp.20-26
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• 2023

Lab-on-a-disc is a circular disc shape of cartridge that can be used for blood-based liquid biopsy to diagnose an early stage of cancer. Currently, liquid biopsies are regarded as a time-consuming process, and require sophisticated skills to precisely separate cell-free DNA (cfDNA) and circulating tumor cells (CTCs) floating in the bloodstream for accurate diagnosis. However, by applying the lab-on-a-disc to liquid biopsy, the entire process can be operated automatically. To do so, the lab-on-a-disc should be designed to prevent blood leakage during the centrifugation, transport, and dilution of blood inside the lab-on-a-disc in the process of liquid biopsy. In this study, the main components of lab-on-a-disc for liquid biopsy are fabricated by injection molding for mass production, and ultrasonic welding is employed to ensure the bonding strength between the components. To guarantee accurate ultrasonic welding, the flatness of the components is optimized numerically by using the response surface methodology with four main injection molding processing parameters, including the mold & resin temperatures, the injection speed, and the packing pressure. The 27 times finite element analyses using Moldflow® reveal that the injection time and the packing pressure are the critical factors affecting the flatness of the components with an optimal set of values for all four processing parameters. To further improve the flatness of the lab-on-a-disc components for stable mass production, a quarter-disc shape of lab-on-a-disc with a radius of 75 mm is used instead of a full circular shape of the disc, and this significantly decreases the standard deviation of flatness to 30% due to the reduced overall length of the injection molded components by one-half. Moreover, it is also beneficial to use a quarter disc shape to manage the deviation of flatness under 3 sigma limits.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.23-31
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• 2010

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.35-45
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• 2009

Emergency medical center(EMC) is the place for patients who need medical treatment immediately due to a disease, childbirth, or all sorts of accidents. Currently, most of EMCs use temporary beds because regular EMC beds cannot afford to serve all incoming patients. However, since it decreases the quality of service(QoS) of EMC patients and their guardians and efficiency of the EMC, some improvements are highly required to diminish the usage of temporary beds. The system duration time is one of the typical QoSs. This thesis proposes the information which is critical to make a better decision for cut down the number of temporary beds without sacrificing QoS of patients. The key point is to control the duration time of medical treatments for the consultation and hospitalization process, since it is the major reason of overcrowding in EMC and the usage of temporary beds. In this paper, we proposed an Arena simulation model reflecting real world substantially. Arena is one of the most widely accepted simulation softwares in the world. Using the developed model, we can obtain the optimal EMC operation parameters through simulation experiments. Optquest, included in the Arena, is used to make the developed simulation model collaborate with an optimization model. The results showed one can determine the set of optimal operation parameters decreasing the required number of temporary beds without deteriorating EMC patient's QoS.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.117-125
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• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.515-532
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• 2023

Bentonite, predominantly consists of expandable clay minerals, is considered to be the suitable buffering material in high-level radioactive waste disposal repository due to its large swelling property and low permeability. Additionally, the bentonite has large cation exchange capacity and specific surface area, and thus, it effectively retards the transport of leaked radionuclides to surrounding environments. This study aims to review the thermodynamic sorption models for four radionuclides (U, Am, Se, and Eu) and eight bentonites. Then, the thermodynamic sorption models and optimized sorption parameters were precisely analyzed by considering the experimental conditions in previous study. Here, the optimized sorption parameters showed that thermodynamic sorption models were related to experimental conditions such as types and concentrations of radionuclides, ionic strength, major competing cation, temperature, solid-to-liquid ratio, carbonate species, and mineralogical properties of bentonite. These results implied that the thermodynamic sorption models suggested by the optimization at specific experimental conditions had large uncertainty for application to various environmental conditions.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.427-442
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• 2011

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1914-1922
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• 2004

This paper provides a comprehensive study of optimum design of a helicopter tailrotor driveshaft made of the flexible matrix composites (FMCs). Since the driveshaft transmits power while subjected to large bending deformation due to aerodynamic loadings, the FMCs can be ideal for enhancing the drivetrain performance by absorbing the lateral deformation without shaft segmentation. However, the increased lateral flexibility and high internal damping of the FMCs may induce whirling instability at supercritical operating conditions. Thus, the purpose of optimization in this paper is to find a set of tailored FMC parameters that compromise between the lateral flexibility and the whirling stability while satisfying several criteria such as torsional buckling safety and the maximum shaft temperature at steadystate conditions. At first, the drivetrain was modeled based on the finite element method and the classical laminate theory with complex modulus approach. Then, an objective function was defined as a combination of an allowable bending deformation and external damping and a genetic algorithm was applied to search for an optimum set with respect to ply angles and stack sequences. Results show that an optimum laminate consists of two groups of layers: (i) one has ply angles well below 45$^{\circ}$ and the other far above 45$^{\circ}$ and (ii) the number of layers with low ply angles is much bigger than that with high ply angles. It is also found that a thick FMC shaft is desirable for both lateral flexibility and whirling stability. The genetic algorithm was effective in converging to several local optimums, whose laminates exhibit similar patterns as mentioned above.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.844-847
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• 1997

In this paper, scheduling problem is dealt for the minimization of due date penalty for the customer order. Multiproduct batch processes have been dealt with for their suitability for high value added low volume products. Their scheduling problems take minimization of process operation for objective function, which is not enough to meet the customer satisfaction and the process efficiency simultaneously because of increasing requirement of fast adaptation for rapid changing market condition. So new target function has been suggested by other researches to meet two goals. Penalty function minimization is one of them. To present more precisely production scheduling, we develop new scheduling model with penalty function of earliness and tardiness We can find many real cases that penalty parameters are divergent by the difference between the completion time of operation and due date. That is to say, the penalty parameter values for the product change by the customer demand condition. If the order charges different value for due date, we can solve it with the due date period. The period means the time scope where penalty parameter value is 0. If we make use of the due date period, the optimal sequence of our model is not always same with that of fixed due date point. And if every product have due date period, due date of them are overlapped which needs optimization for the maximum profit and minimum penalty. Due date period extension can be enlarged to makespan minimization if every product has the same abundant due date period and same penalty parameter. We solve this new scheduling model by simulated annealing method. We also develop the program, which can calculate the optimal sequence and display the Gantt chart showing the unit progress and time allocation only with processing data.