• Smart Structures and Systems
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• v.24 no.3
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• pp.303-317
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• 2019

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.1
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• pp.38-45
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• 2019

In this paper, the stability of the tunnel boring machine (TBM), used in tunnel excavation, according to the thrust force of the thrust jack was investigated. The existing hydraulic cylinder analysis method is fluid-structure interaction (FSI) analysis, where all of the flow setting and dynamic characteristics should be considered. Therefore, there is a need for a method to solve this problem simply and quickly. To facilitate this, the theoretical pressure in the hydraulic cylinder was calculated and compared with the analytical and experimental results. In the case of the analysis, the pressure generated inside the cylinder was analyzed statically, considering the operating characteristics of the shield TBM, and the stress and pressure were calculated. This method simplifies the analysis environment and shortens the analysis time compared to the existing analysis method. The obtained theoretical and analytical data were compared with the measured data during actual tunneling, and the analysis and experimental data showed a relative error of approximately 23.89%.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.161-167
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• 2021

This paper presents the method to quantitatively evaluate the uncertainty of the semi-empirical Bidirectional Reflectance Distribution Function (BRDF) model for Himawari-8/AHI. The uncertainty of BRDF modeling was affected by various issues such as assumption of model and number of observations, thus, it is difficult that evaluating the performance of BRDF modeling using simple uncertainty equations. Therefore, in this paper, Monte-Carlo method, which is most dependable method to analyze dynamic complex systems through iterative simulation, was used. The 1,000 input datasets for analyzing the uncertainty of BRDF modeling were generated using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Model (RTM) simulation with MODerate Resolution Imaging Spectroradiometer (MODIS) BRDF product. Then, we randomly selected data according to the number of observations from 4 to 35 in the input dataset and performed BRDF modeling using them. Finally, the uncertainty was calculated by comparing reproduced surface reflectance through the BRDF model and simulated surface reflectance using 6S RTM and expressed as bias and root-mean-square-error (RMSE). The bias was negative for all observations and channels, but was very small within 0.01. RMSE showed a tendency to decrease as the number of observations increased, and showed a stable value within 0.05 in all channels. In addition, our results show that when the viewing zenith angle is 40° or more, the RMSE tends to increase slightly. This information can be utilized in the uncertainty analysis of subsequently retrieved geophysical variables.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.4
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• pp.43-52
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• 2021

In this study, performance modeling and simulation of a gas turbine engine, a constituent module, was performed for the integrated control of the CODOG structure, mechanical propulsion systems. The engine model used MATLAB/Simulink to facilitate integration with the host controller and other components, and was configured to enable input/output settings suitable for the system configuration and purpose. In general, engine manufacturers do not provide performance data for the engine and components. Therefore, as a modeling method for a gas turbine, a CMF method that obtains performance data by scaling the map of components was applied. Using the generated model and simulation program, steady-state and dynamic simulation analysis tests were performed, and reliability within 5% of the maximum error was secured for the final output of power.

• Current Optics and Photonics
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• v.6 no.5
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• pp.445-452
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• 2022

We developed an adaptive optics test bench using an optical simulator and two rotating phase plates that mimicked the atmospheric turbulence at Bohyunsan Observatory. The observatory was reported to have a Fried parameter with a mean value of 85 mm and standard deviation of 13 mm, often expressed as 85 ± 13 mm. First, we fabricated several phase plates to generate realistic atmospheric-like turbulence. Then, we selected a pair from among the fabricated phase plates to emulate the atmospheric turbulence at the site. The result was 83 ± 11 mm. To address dynamic behavior, we emulated the atmospheric disturbance produced by a wind flow of 8.3 m/s by controlling the rotational speed of the phase plates. Finally, we investigated how closely the atmospheric disturbance simulation emulated reality with an investigation of the measurements on the optical table. The verification confirmed that the simulator showed a Fried parameter of 87 ± 15 mm as designed, but a little slower wind velocity (7.5 ± 2.5 m/s) than expected. This was because of the nonlinear motion of the phase plates. In conclusion, we successfully mimicked the atmospheric disturbance of Bohyunsan Observatory with an error of less than 10% in terms of Fried parameter and wind velocity.

• Wind and Structures
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• v.34 no.5
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• pp.421-435
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• 2022

To investigate the non-Gaussian properties of fluctuating wind pressures and the error margin of extreme wind loads on a long-span curved roof with matching and mismatching ratios of turbulence integral scales to depth (L_u^x/D), a series of synchronized pressure tests on the rigid model of the complex curved roof were conducted. The regions of Gaussian distribution and non-Gaussian distribution were identified by two criteria, which were based on the cumulative probabilities of higher-order statistical moments (skewness and kurtosis coefficients, S_k and K_u) and spatial correlation of fluctuating wind pressures, respectively. Then the characteristics of fluctuating wind-loads in the non-Gaussian region were analyzed in detail in order to understand the effects of turbulence integral-scale. Results showed that the fluctuating pressures with obvious negative-skewness appear in the area near the leading edge, which is categorized as the non-Gaussian region by both two identification criteria. Comparing with those in the wind field with matching L_u^x/D, the range of non-Gaussian region almost unchanged with a smaller L_u^x/D, while the non-Gaussian features become more evident, leading to higher values of S_k, Ku and peak factor. On contrary, the values of fluctuating pressures become lower in the wind field with a smaller L_u^x/D, eventually resulting in underestimation of extreme wind loads. Hence, the matching relationship of turbulence integral scale to depth should be carefully considered as estimating the extreme wind loads of long-span roof by wind tunnel tests.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.937-948
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• 2022

Agricultural operations are performed in uneven environments by attaching an implement on the 3-point hitch of a tractor. A high load is thus placed on the 3-point hitch, and fatigue and failure of the hitch may occur during agricultural operations. In this study, a dynamic simulation model was developed to predict the load occurring on the eyebolt of a 3-point hitch, which is the main damaged component. The simulation model was developed and validated using agricultural data as simulation input and validation data. The dynamics model was developed using the specifications of a 78 kW class tractor. A measurement system was constructed to measure the simulation input and validation data. The simulation model was validated using a traction load on an eye bolt, which was measured during plow tillage operation. The measurement results showed that the average traction load on the left and right lower link and the top link were 8,099.97, 4,943.06, and 636.11 N, respectively. The simulation results and the measured traction load on the left eyebolt were respectively 610.30 and 597.15 N. The simulation results and measured traction load on the left eyebolt were respectively 1,179.78, and 1,145.06 N. The error between the simulation and measurement data was roughly 2% on the left eyebolt and 3% on the right eyebolt.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.