• Elastomers and Composites
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• v.58 no.1
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• pp.44-56
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• 2023

Additive manufacturing (AM) or three-dimensional (3D) printing of metals has been drawing significant attention due to its reliability, usefulness, and low cost with rapid prototyping. Among the various AM technologies, fused deposition modeling (FDM) or fused filament fabrication is receiving much interest because of its simple manufacturing processing, low material waste, and cost-effective equipment. FDM technology uses metal-filled polymer filaments for 3D printing, followed by debinding and sintering to fabricate complex metal parts. An efficient binder is essential for producing polymer filaments and the thermal post-processing of printed objects. This study involved an in-depth investigation of and a fabrication route for a novel multi-component binder system with steel alloy powder (45 vol.%) ranging from filament fabrication and 3D printing to debinding and sintering. The binder system consisted of polyvinyl pyrrolidone (PVP) as a binder and thermoplastic polyurethane (TPU) and polylactic acid (PLA) as a carrier. The PVP binder held the metal components tightly by maintaining their stoichiometry, and the TPU and PLA in the ratio of 9:1 provided flexibility, stiffness, and strength to the filament for 3D printing. The efficacy of the binder system was examined by fabricating 3D-printed cubic structures. The results revealed that the thermal debinding and sintering processes effectively removed the binder/carrier from the cubic structures, resulting in isotropic shrinkage of approximately 15.8% in all directions. The scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) patterns displayed the microstructure behavior, phase transition, and elemental composition of the 3D cubic structure.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.69-90
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• 2022

As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers. To this end, I conducted various experiments on major factors affecting the performance of automatic classification, focusing on feature selection techniques, for the purpose of automatically allocating the classification categories on the National Research Foundation of Korea's Academic Research Classification Scheme to domestic journal papers. As a result, the automatic classification of domestic journal papers, which are imbalanced datasets of the real environment, showed that a fairly good level of performance can be expected using more simple classifiers, feature selection techniques, and relatively small training sets.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.50.3-51
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• 2021

The progenitor of Type Ia supernovae is largely expected as a close binary system of a carbon/oxygen white dwarf (WD) primary and its secondary non-degenerate (single degenerate; SD) or degenerate companion (double degenerate; DD). Here we present a high-cadence monitoring observation of SN 2021hpr in a spiral galaxy, NGC 3147. SN 2021hpr shows typical characteristics as a normal type Ia supernova from its photometric (Δm₁₅(B)=1.01±0.03, dust free M_B,max=-19.45±0.02) and spectroscopic data. To investigate its progenitor system, we fit the early part of BVRI-band light curve simultaneously with a combined version of ejecta-companion and simple power-law model. As a result, we found a significant feature of an early excess possibly from a 7.63±0.52R_⊙-sized companion at the optimal viewing angle while the fit is not successful at the common viewing angle. No possible red sources brighter than F555W=-7.01 AB mag is detected at the SN location in Hubble Space Telescope (HST) pre-explosion images, excluding massive stars with initial mass of >16M_⊙ as companions. We suggest the progenitor system of SN 2021hpr can be a fairly large companion such as a main sequence, a low mass subgiant, and a helium giant star. In addition, a possibility of the ejecta-Disk Originated Matter (DOM) interaction for the DD scenario considering linearly-rising early flux still remains.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Asia Marketing Journal
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• v.16 no.1
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• pp.215-228
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• 2014

The automotive industry plays a significant role in the global economy. One of the reasons is that this industry compasses every aspects of the value chain - from raw materials to design and development, manufacturing, sales and services, and even disposal. Thus, the industry needs significant upfront capital investment and requires years of R&D and market development. As a result, this industry is dominated by a handful of global players and it is not easy for a new entrant to enter this industry. Furthermore, success is even more difficult to achieve. How did Hyundai Motor make it in this tough marketplace? Can it continue against all odds? The CAGR for last 5 years is 12% and it stands at 6th in the world. Compared to other global brands, Hyundai has geographically well-balanced sales portfolio. The quality improvement is outstanding. The brand performance follows these quality and sales improvements. Yet, the global competition is ever intensifying. Now, it is the time to step up once more. The next strategic goal needs fundamental shift toward brand and marketing-focus. In constructing global marketing strategy, Hyundai Motor's vision is "Lifetime partner in mobility and beyond" and its goal is global top 3 brand by year 2015 through modern premium brand image and selling 5 million vehicles. The target brand positioning of Hyundai Motor is the leading position in premium dimension and stylish/modern dimension. The global brand strategy framework is based on the brand direction of "Modern Premium" and is designed to deliver core brand identity (i.e., Simple, Creative, Caring) to customers. In order to manage brand performance, Hyundai's marketing platformalso includes marketing performance management, brand performance management, and market driven organization. From this diagnosis, Hyundai Motor is well posed to build a strong brand. Nevertheless, there are still challenges ahead from consumer, technology, competitor, and macro-environment perspectives. To overcome these threats, the bases of competition for all successful automotive brands are various differentiation factors, including technology, performance, value proposition, or heritage. Hyundai Motor is well prepared so far. However, it is not tested against time yet whether Hyundai can overcome these unforeseeable major threats. Hyundai is trying to find the solution from a strong brand, while believing in "New Thinking. New Possibilities."

• Analytical Science and Technology
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• v.36 no.5
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• pp.216-223
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• 2023

Aluminium (Al) is one of the major elements in rocks and its concentration can be varied, depending on different rock types as well as sources. The present study aimed to propose an analytical method based on the UV-Vis as a cheap, simple, and common instrument equipped in most laboratories for Al quantification in rocks after the microwave assisted acid digestion. The aluminone and 8-hydroxyquinoline were investigated for the colorimetric assay. The results show that the 8-hydroxyquinoline reagent was more favorable in terms of the minimized affects of the potential interferences present in the digested solutions, i.e., Fe³⁺, Si⁴⁺ and F^-. The calibration curve was constructed from 0.10 mg/L to 3.00 mg/L with the goodness of linearity (R² = 0.9996). The limits of detection and quantification (LOD and LOQ) were estimated, i.e., 0.029 mg/L and 0.087 mg/L, respectively. The 8-hydroxyquinoline was applied to real rock samples, demonstrating favorable precision (RSD = 0.34 %-1.8 %) and no remarkable differences were found compared to the inductively coupled plasma-mass spectrometry (ICP-MS) as a reference measurement approach.

• Analytical Science and Technology
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• v.37 no.2
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• pp.87-97
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• 2024

The appropriate intake of fluoride (F^-) is beneficial to human health; however, the over-consumption can result in various potentially harmful effects. This study compared different colorimetric reagents, i.e., aluminium-xylenol orange (Al-XO), zirconium-xylenol orange (Zr-XO), and zirconium-alizarin red S (Zr-ARS), for fluoride measurements by the UV-Vis, in terms of reaction mechanisms, method sensitivity, and interferences from aluminium and ferric ions. The colorimetric procedures were optimized, and the analytical methods were evaluated. The goodness of linearity (R² > 0.998) was obtained for all three assays within the concentration range of 1.0-20.0 mg/L fluoride in deionized water, in which the method sensitivity followed the descending order of Zr-XO > Al-XO > Zr-ARS. The Zr-XO was applied for determining the fluoride in different tea extracts in water (90 ℃ and 60-minute-brewing) and black tea demonstrated the highest fluoride content (3.0-3.6 mg/L). The effects of brewing time and temperature on the release of fluoride in the tea extracts were also investigated, indicating these are critical factors for the fluoride extraction. This study highlighted the application potentials of the UV-Vis measurement as a simple, convenient, and cheap analytical approach and discussed different colorimetric reagents used for fluoride determination in tea extracts in the context that the UV-Vis spectrophotometers are commonly equipped in most laboratories.

• Transactions of the Korean hydrogen and new energy society
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• v.35 no.2
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• pp.152-161
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• 2024

In this study, a fuel cell system for future defense unmanned vehicles was designed and validated. A Co/Al₂O₃-Ni foam catalyst for NaBH₄ hydrolysis was characterized using several analytical methods. A NaBH₄ hydrogen generation system with the Co/Al₂O₃-Ni foam catalyst continuously generated hydrogen at elevated reaction temperatures. The fuel cell system with the NaBH₄ hydrogen generation system was designed and tested. The performance of the fuel cell system was comparable to that of the fuel cell system using pure hydrogen. Therefore, the fuel cell system with the NaBH₄ hydrogen generation system is a suitable power source for future defense unmanned vehicles owing to its easy refueling and simple system.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.