• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.1
• /
• pp.34-41
• /
• 2021

Interventional radiology and minimally invasive surgery both require a precisely shaped microcatheter. Microcatheters are manufactured using polymer extrusion processes with a die and puller. The manufacturing parameters and die geometry greatly influence the profile of the extrudate and designing dies using a trial-and-error process is expensive and requires a lot of time. Therefore, predicting the profile of the extrudate is important for manufacturing microcatheters. This study investigates the effects of die design and geometry on the profile of the extrudate. The profiles of the extrudate are predicted using ANSYS Polyflow with respect to the different die geometries. The outer and inner diameters and wall thickness of the predicted extrudate are compared to those of a target extrudate. The die swell of melt polymer and the effect of the pulling are both examined. Optimized die designs are suggested for manufacturing the target extrudate.

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.97-114
• /
• 2022

Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

• Smart Structures and Systems
• /
• v.30 no.4
• /
• pp.421-432
• /
• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Wind and Structures
• /
• v.36 no.6
• /
• pp.423-434
• /
• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.3
• /
• pp.205-213
• /
• 2023

An optimal design of a simple beam-shaped flexure hinge mount supporting an optical mirror is presented. An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. This side-supporting mount is flexible in the radial direction and rigid for the remaining degrees of freedom to support the mirror without transferring thermal load. Through thermo-elastic, optical and eigenvalue analysis, opto-mechanical performance was predicted to establish the objective functions for optimization. The key design parameters for this flexure are the thickness and length. To find the optimal values of design parameters, response surface analysis was performed using the design of experiment based on nested FCD. Optimal design candidates were derived from the response surface analysis, and the optimal design shape was confirmed through Opto-mechanical performance validation analysis.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.21-27
• /
• 2013

Construction safety is a predominant hindrance in in-situ workflow and considered an unresolved issue. Current methods used for safety optimization and prediction, with limited exceptions, are paper-based, thus error prone, as well as time and cost ineffective. In an attempt to exploit the potential of BIM for safety, the objective of the proposed methodology is to automatically predict hazardous on-site conditions related to the route that the dozers follow during the different phases of the project. For that purpose, safety routes used by construction equipment from an origin to multiple destinations are computed using video cameras and their cycle times are calculated. The cycle times and factors; including weather and light conditions, are considered to be independent and identically distributed random variables (iid); and simulated using the Arena software. The simulation clock is set to 100 to observe the minor changes occurring due to external parameters. The validation of this technology explores the capabilities of BIM combined with simulation for enhancing productivity and improving safety conditions a-priori. Preliminary results of 262 measurements indicate that the proposed methodology has the potential to predict with 87% the location of exclusion zones. Also, the cycle time is estimated with an accuracy of 89%.

• Journal of the Korean Society of Safety
• /
• v.38 no.6
• /
• pp.9-15
• /
• 2023

Hydrogen is gaining attention as a sustainable and renewable energy source, potentially replacing fossil fuels. Its high diffusivity, wide flammable range, and low ignition energy make it prone to ignition even with minimal friction, potentially leading to fire and explosion risks. Workplaces manage ignition risks by classifying areas with explosive atmospheres. However, the effective installation of a blast wall can significantly limit the spread of hydrogen, thereby enhancing workplace safety. To optimize the wall installation of this barrier, we employed the response surface methodology (RSM), considering variables such as wall distance, height, and width. We performed 17 simulations using the Box-Behnken design, conducted using FLACS software. This process yielded two objective functions: explosion likelihood near the barrier and explosion overpressure affecting the blast wall. We successfully achieved the optimal solution using multi-objective optimization for these two functions. We validated the optimal solution through verification simulations to ensure reliability, maintaining a margin of error of 5%. We anticipated that this method would efficiently determine the most effective installation of a blast wall while enhancing workplace safety.

• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.1
• /
• pp.63-73
• /
• 2024

NeQuick G is the ionosphere model utilized by Galileo single-frequency users to estimate the ionospheric delay on each user-satellite link. The model is characterized by the effective ionization level (Az) index, determined by a modified dip latitude (MODIP) and broadcast coefficients derived from daily global space weather observations. However, globally fitted Az coefficients may not accurately represent ionosphere within local area. This study introduces a method for regional ionospheric modeling that searches for locally optimized Az coefficients. This approach involves fitting TEC output from NeQuick G to TEC data collected from GNSS stations around Korea under various ionospheric conditions including different seasons and both low and high solar activity phases. The optimized Az coefficients enable calculation of the Az index at any position within a region of interest, accounting for the spatial variability of the Az index in a polynomial function of MODIP. The results reveal reduced TEC estimation errors, particularly during high solar activity, with a maximum reduction in the RMS error by 85.95%. This indicates that the proposed method for NeQuick G can effectively model various ionospheric conditions in local areas, offering potential applications in GNSS performance analyses for local areas by generating various ionospheric scenarios.

• Journal of Radiation Industry
• /
• v.17 no.4
• /
• pp.551-557
• /
• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Nuclear Engineering and Technology
• /
• v.56 no.10
• /
• pp.4042-4049
• /
• 2024

A new automatic group structure optimization method based on the perturbation theory was proposed for the few-group structure in two-step nuclear design procedure for VHTR. It applies the sensitivity coefficient of the perturbation theory which includes not only the effect of the cross section on the multiplication factor but also the adjoint weighted reaction rate. The sensitivity coefficient of the fine group for the multiplication factor was calculated and the group boundary for a few-group can be determined so that the summation of the fine group sensitivity for a few-group should be evenly distributed over every few-group. This method was successfully implemented in the ABGO code. VHTR-350 and MiHTR 2D core were used to investigate the performance and applicability of the proposed method. The code generated the new group structures for two cores and the error of the multiplication and reaction rate by the new group structure was compared with the result by the fine group structure. The comparisons indicate that the new group structure by the proposed method can provide the multiplication factor and reaction rates comparable to the existing group structure and more accurate results than the group structure obtained using the Contributon theory.